WrapperMethods_FeatureEngineering.ipynb

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    "## Wrapper Methods: Feature Selection for Obesity Prediction\n",
    "\n",
    "In this project, you'll analyze data from a survey conducted by Fabio Mendoza Palechor and Alexis de la Hoz Manotas that asked people about their eating habits and weight. The data was obtained from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition+). Categorical variables were changed to numerical ones in order to facilitate analysis.\n",
    "\n",
    "First, you'll fit a logistic regression model to try to predict whether survey respondents are obese based on their answers to questions in the survey. After that, you'll use three different wrapper methods to choose a smaller feature subset.\n",
    "\n",
    "You'll use sequential forward selection, sequential backward floating selection, and recursive feature elimination. After implementing each wrapper method, you'll evaluate the model accuracy on the resulting smaller feature subsets and compare that with the model accuracy using all available features."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating a Logistic Regression Model\n",
    "\n",
    "The data set `obesity` contains 18 predictor variables. Here's a brief description of them.\n",
    "\n",
    "* `Gender` is `1` if a respondent is male and `0` if a respondent is female.\n",
    "* `Age` is a respondent's age in years.\n",
    "* `family_history_with_overweight` is `1` if a respondent has family member who is or was overweight, `0` if not.\n",
    "* `FAVC` is `1` if a respondent eats high caloric food frequently, `0` if not.\n",
    "* `FCVC` is `1` if a respondent usually eats vegetables in their meals, `0` if not.\n",
    "* `NCP` represents how many main meals a respondent has daily (`0` for 1-2 meals, `1` for 3 meals, and `2` for more than 3 meals).\n",
    "* `CAEC` represents how much food a respondent eats between meals on a scale of `0` to `3`.\n",
    "* `SMOKE` is `1` if a respondent smokes, `0` if not.\n",
    "* `CH2O` represents how much water a respondent drinks on a scale of `0` to `2`.\n",
    "* `SCC` is `1` if a respondent monitors their caloric intake, `0` if not.\n",
    "* `FAF` represents how much physical activity a respondent does on a scale of `0` to `3`.\n",
    "* `TUE` represents how much time a respondent spends looking at devices with screens on a scale of `0` to `2`.\n",
    "* `CALC` represents how often a respondent drinks alcohol on a scale of `0` to `3`.\n",
    "* `Automobile`, `Bike`, `Motorbike`, `Public_Transportation`, and `Walking` indicate a respondent's primary mode of transportation. Their primary mode of transportation is indicated by a `1` and the other columns will contain a `0`.\n",
    "\n",
    "The outcome variable, `NObeyesdad`, is a `1` if a patient is obese and a `0` if not.\n",
    "\n",
    "Use the `.head()` method and inspect the data."
   ]
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     "text": [
      "   Gender   Age  family_history_with_overweight  FAVC  FCVC  NCP  CAEC  SMOKE  \\\n",
      "0       0  21.0                               1     0   2.0  3.0     1      0   \n",
      "1       0  21.0                               1     0   3.0  3.0     1      1   \n",
      "2       1  23.0                               1     0   2.0  3.0     1      0   \n",
      "3       1  27.0                               0     0   3.0  3.0     1      0   \n",
      "4       1  22.0                               0     0   2.0  1.0     1      0   \n",
      "\n",
      "   CH2O  SCC  FAF  TUE  CALC  Automobile  Bike  Motorbike  \\\n",
      "0   2.0    0  0.0  1.0     0           0     0          0   \n",
      "1   3.0    1  3.0  0.0     1           0     0          0   \n",
      "2   2.0    0  2.0  1.0     2           0     0          0   \n",
      "3   2.0    0  2.0  0.0     2           0     0          0   \n",
      "4   2.0    0  0.0  0.0     1           0     0          0   \n",
      "\n",
      "   Public_Transportation  Walking  NObeyesdad  \n",
      "0                      1        0           0  \n",
      "1                      1        0           0  \n",
      "2                      1        0           0  \n",
      "3                      0        1           0  \n",
      "4                      1        0           0  \n"
     ]
    }
   ],
   "source": [
    "# https://archive.ics.uci.edu/ml/datasets/Estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition+\n",
    "\n",
    "# Load the data\n",
    "obesity = pd.read_csv(\"obesity.csv\")\n",
    "\n",
    "# Inspect the data\n",
    "print(obesity.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into `X` and `y`\n",
    "\n",
    "In order to use a linear regression model, you'll need to split the data into two parts: the predictor variables and an outcome variable. Do this by splitting the data into a DataFrame of predictor variables called `X` and a Series of outcome variables `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X (predictor variables): (2111, 18)\n",
      "Shape of y (outcome variable): (2111,)\n"
     ]
    }
   ],
   "source": [
    "X = obesity.drop('NObeyesdad', axis=1)  # Drop the outcome column to create X\n",
    "y = obesity['NObeyesdad']  # Outcome column\n",
    "\n",
    "# Verify the shapes of X and y\n",
    "print(f\"Shape of X (predictor variables): {X.shape}\")\n",
    "print(f\"Shape of y (outcome variable): {y.shape}\")\n",
    "\n",
    "# Split data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression model\n",
    "\n",
    "Create a logistic regression model called `lr`. Include the parameter `max_iter=1000` to make sure that the model will converge when you try to fit it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(max_iter=1000)\n"
     ]
    }
   ],
   "source": [
    "# Create the logistic regression model\n",
    "lr = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Display the initialized model\n",
    "print(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model\n",
    "\n",
    "Use the `.fit()` method on `lr` to fit the model to `X` and `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model accuracy\n",
    "\n",
    "A model's _accuracy_ is the proportion of classes that the model correctly predicts. is Compute and print the accuracy of `lr` by using the `.score()` method. What percentage of respondents did the model correctly predict as being either obese or not obese? You may want to write this number down somewhere so that you can refer to it during future tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequential Forward Selection\n",
    "\n",
    "Now that you've created a logistic regression model and evaluated its performance, you're ready to do some feature selection. \n",
    "\n",
    "Create a sequential forward selection model called `sfs`. \n",
    "* Be sure to set the `estimator` parameter to `lr` and set the `forward` and `floating` parameters to the appropriate values. \n",
    "* Also use the parameters `k_features=9`, `scoring='accuracy'`, and `cv=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "\n",
    "sfs = SFS(estimator=lr, \n",
    "          k_features=9, \n",
    "          forward=True, \n",
    "          floating=False, \n",
    "          scoring='accuracy', \n",
    "          cv=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model\n",
    "\n",
    "Use the `.fit()` method on `sfs` to fit the model to `X` and `y`. This step will take some time (not more than a minute) to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          k_features=(9, 9), scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SequentialFeatureSelector<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          k_features=(9, 9), scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          k_features=(9, 9), scoring='accuracy')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect the results\n",
    "\n",
    "Now that you've run the sequential forward selection algorithm on the logistic regression model with `X` and `y` you can see what features were chosen and check the model accuracy on the smaller feature set. Print `sfs.subsets_[9]` to inspect the results of sequential forward selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combination of SFS (ACC: 0.783): ('Gender', 'Age', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'FAF', 'Motorbike')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Best combination of SFS (ACC: %.3f): %s\\n' % (sfs.subsets_[9]['avg_score'], sfs.subsets_[9]['feature_names']))\n",
    "#print(sfs.subsets_[9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize model accuracy\n",
    "\n",
    "It can be helpful to visualize the results of sequential forward selection and see how accuracy is affected as each feature is added. Use the code `plot_sfs(sfs.get_metric_dict())` to plot the model accuracy as a function of the number of features used. Make sure to show your plot as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidalmagro/Documents/projects/ml_env/lib/python3.12/site-packages/mlxtend/feature_selection/sequential_feature_selector.py:895: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n",
      "  std_err = scipy.stats.sem(ary)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>,\n",
       " <Axes: xlabel='Number of Features', ylabel='Performance'>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sfs(sfs.get_metric_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13f6e9cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0be0e0",
   "metadata": {},
   "source": [
    "## Sequential Backward Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sequential forward selection was able to find a feature subset that performed marginally better than the full feature set. Let's use a different sequential method and see how it compares.\n",
    "\n",
    "Create a sequential backward selection model called `sbs`. \n",
    "* Be sure to set the `estimator` parameter to `lr` and set the `forward` and `floating` parameters to the appropriate values.\n",
    "* Also use the parameters `k_features=7`, `scoring='accuracy'`, and `cv=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sbs = SFS(estimator=lr,\n",
    "          k_features=7,\n",
    "          forward=False,\n",
    "          floating=False,\n",
    "          scoring='accuracy',\n",
    "          cv=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model\n",
    "\n",
    "Use the `.fit()` method on `sbs` to fit the model to `X` and `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          forward=False, k_features=(7, 7), scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SequentialFeatureSelector<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          forward=False, k_features=(7, 7), scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          forward=False, k_features=(7, 7), scoring='accuracy')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sbs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect the results\n",
    "\n",
    "Now that you've run the sequential backward selection algorithm on the logistic regression model with `X` and `y` you can see what features were chosen and check the model accuracy on the smaller feature set. Print `sbs.subsets_[7]` to inspect the results of sequential backward selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combination of SBS (ACC: 0.764): ('Age', 'family_history_with_overweight', 'FAVC', 'FCVC', 'CAEC', 'CALC', 'Public_Transportation')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Best combination of SBS (ACC: %.3f): %s\\n' % (sbs.subsets_[7]['avg_score'], sbs.subsets_[7]['feature_names']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize model accuracy\n",
    "\n",
    "You can visualize the results of sequential backward floating selection just as you did with sequential forward selection. Use the code `plot_sfs(sbs.get_metric_dict())` to plot the model accuracy as a function of the number of features used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidalmagro/Documents/projects/ml_env/lib/python3.12/site-packages/mlxtend/feature_selection/sequential_feature_selector.py:895: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n",
      "  std_err = scipy.stats.sem(ary)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>,\n",
       " <Axes: xlabel='Number of Features', ylabel='Performance'>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sfs(sbs.get_metric_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far you've tried two different sequential feature selection methods. Let's try one more: recursive feature elimination. First you'll standardize the data, then you'll fit the RFE model and inspect the results.\n",
    "\n",
    "At a later step of this project, you'll need to be able to access feature names. Enter the code `features = X.columns` for use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the data\n",
    "\n",
    "Before doing applying recursive feature elimination it is necessary to standardize the data. Standardize `X` and save it as a DataFrame by creating a `StandardScaler()` object and using the `.fit_transform()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive feature elimination model\n",
    "\n",
    "Create an `RFE()` object that selects `8` features. Be sure to set the `estimator` parameter to `lr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe = RFE(estimator=lr, n_features_to_select=7)  # Select 7 features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model\n",
    "\n",
    "Fit the recursive feature elimination model to `X` and `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RFE(estimator=LogisticRegression(max_iter=1000), n_features_to_select=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RFE<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.RFE.html\">?<span>Documentation for RFE</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RFE(estimator=LogisticRegression(max_iter=1000), n_features_to_select=7)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RFE(estimator=LogisticRegression(max_iter=1000), n_features_to_select=7)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect chosen features\n",
    "\n",
    "Now that you've fit the RFE model you can evaluate the results. Create a list of chosen feature names and call it `rfe_features`. You can use a list comprehension and filter the features in `zip(features, rfe.support_)` based on whether their support is `True` (meaning the model kept them) or `False` (meaning the model eliminated them)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features by RFE: Index(['Age', 'family_history_with_overweight', 'FAVC', 'FCVC', 'CAEC', 'SCC',\n",
      "       'Automobile'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(f\"Selected features by RFE: {features[rfe.support_]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model accuracy\n",
    "\n",
    "Use the `.score()` method on `rfe` and print the model accuracy after doing recursive feature elimination. How does this compare to the model's accuracy on all available features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFE with LR model Accuracy: 0.7626717195641876\n"
     ]
    }
   ],
   "source": [
    "print(f\"RFE with LR model Accuracy: {rfe.score(X_scaled, y)}\")"
   ]
  }
 ],
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