wikipedia-bayes-example

Table of contents

• Data
  • Train data (train.csv)
  • Prediction data (predict.csv)
  • Prediction result
• Using GUI (Orange3)
  • wikipedia-bayes-example.ows
  • Data Table (train)
  • Test & Score
  • Confusion Matrix
  • Tree Viewer
  • Data Table (predict)
  • Predictions
  • Data Table (result)
• Using code
  • orange-version.py
  • sklearn-version.py
  • ml-from-scratch-version.py

---

Data

Example data from: Wikipedia > Naive_Bayes_classifier > Examples

Train data (train.csv)

 sex    | height (feet) | weight (lbs) | foot size(inches)
--------+---------------+--------------+-----------------
 male   | 6             | 180          | 12
 male   | 5.92 (5'11")  | 190          | 11
 male   | 5.58 (5'7")   | 170          | 12
 male   | 5.92 (5'11")  | 165          | 10
 female | 5             | 100          | 6
 female | 5.5 (5'6")    | 150          | 8
 female | 5.42 (5'5")   | 130          | 7
 female | 5.75 (5'9")   | 150          | 9

Prediction data (predict.csv)

 sex    | height (feet) | weight (lbs) | foot size(inches)
--------+---------------+--------------+-----------------
 ?      | 6             | 130          | 8

Prediction result

female

---

Using GUI (Orange3)

wikipedia-bayes-example.ows

Data Table (train)

Test & Score

Confusion Matrix

Tree Viewer

Data Table (predict)

Predictions

Data Table (result)

---

Using code

orange-version.py

=== train.X ===
[[   6.    180.     12.  ]
 [   5.92  190.     11.  ]
 [   5.58  170.     12.  ]
 [   5.92  165.     10.  ]
 [   5.    100.      6.  ]
 [   5.5   150.      8.  ]
 [   5.42  130.      7.  ]
 [   5.75  150.      9.  ]]

=== train.Y ===
[ 1.  1.  1.  1.  0.  0.  0.  0.]

=== predict.X ===
[[   6.  130.    8.]]

=== results ===
LogisticRegressionLearner: female
NaiveBayesLearner        : female
TreeLearner              : female
RandomForestLearner      : female
KNNLearner               : female
SVMLearner               : female

sklearn-version.py

scikit-learn + pandas

=== train_X ===
   height  weight  foot_size
0    6.00     180         12
1    5.92     190         11
2    5.58     170         12
3    5.92     165         10
4    5.00     100          6
5    5.50     150          8
6    5.42     130          7
7    5.75     150          9

=== train_Y ===
0      male
1      male
2      male
3      male
4    female
5    female
6    female
7    female
Name: sex, dtype: object

=== predict_X ===
   height  weight  foot_size
0       6     130          8

=== results ===
GaussianNB               : female
LogisticRegression       : female
DecisionTreeClassifier   : female
KNeighborsClassifier     : female
RandomForestClassifier   : female
AdaBoostClassifier       : female
SVC                      : female

ml-from-scratch-version.py

ML-From-Scratch

=== train_X ===
[[   6.    180.     12.  ]
 [   5.92  190.     11.  ]
 [   5.58  170.     12.  ]
 [   5.92  165.     10.  ]
 [   5.    100.      6.  ]
 [   5.5   150.      8.  ]
 [   5.42  130.      7.  ]
 [   5.75  150.      9.  ]]

=== train_Y ===
[1 1 1 1 0 0 0 0]

=== predict_X ===
[[  6 130   8]]

=== results ===
ClassificationTree       : female
NaiveBayes               : female
Training: 100% [-----------------------------------------------------------------------------------------] Time: 0:00:00
RandomForest             : female
Training: 100% [-----------------------------------------------------------------------------------------] Time: 0:00:01
XGBoost                  : female
KNN                      : female