diff --git a/.gitmodules b/.gitmodules
index 0c3670415..806dcc2c6 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -2,3 +2,7 @@
 	path = treeple/_lib/sklearn_fork
 	url = https://github.com/neurodata/scikit-learn
 	branch = submodulev3
+[submodule "treeple/_lib_experimental/sklearn_fork"]
+	path = treeple/_lib_experimental/sklearn_fork
+	url = https://github.com/neurodata/scikit-learn.git
+	branch = scarliles/honesty
diff --git a/examples/viss/calibration.ipynb b/examples/viss/calibration.ipynb
new file mode 100644
index 000000000..6054d22a0
--- /dev/null
+++ b/examples/viss/calibration.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "14aa43dc-7c73-4cfc-ad61-1408c64e8dd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X, y = make_classification(\n",
+    "    n_samples=100_000, n_features=20, n_informative=2, n_redundant=2, random_state=42\n",
+    ")\n",
+    "\n",
+    "train_samples = 100  # Samples used for training the models\n",
+    "#train_samples = 400  # Samples used for training the models\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    shuffle=False,\n",
+    "    test_size=100_000 - train_samples,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d9ed0d84-eae1-4820-a7fe-f61de6036940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from sklearn.svm import LinearSVC\n",
+    "\n",
+    "\n",
+    "class NaivelyCalibratedLinearSVC(LinearSVC):\n",
+    "    \"\"\"LinearSVC with `predict_proba` method that naively scales\n",
+    "    `decision_function` output.\"\"\"\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        super().fit(X, y)\n",
+    "        df = self.decision_function(X)\n",
+    "        self.df_min_ = df.min()\n",
+    "        self.df_max_ = df.max()\n",
+    "\n",
+    "    def predict_proba(self, X):\n",
+    "        \"\"\"Min-max scale output of `decision_function` to [0,1].\"\"\"\n",
+    "        df = self.decision_function(X)\n",
+    "        calibrated_df = (df - self.df_min_) / (self.df_max_ - self.df_min_)\n",
+    "        proba_pos_class = np.clip(calibrated_df, 0, 1)\n",
+    "        proba_neg_class = 1 - proba_pos_class\n",
+    "        proba = np.c_[proba_neg_class, proba_pos_class]\n",
+    "        return proba"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6002e5aa-45ab-4513-a3b6-4fba8c890fdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "from sklearn.ensemble import HonestRandomForestClassifier\n",
+    "\n",
+    "\n",
+    "# Define the classifiers to be compared in the study.\n",
+    "#\n",
+    "# Note that we use a variant of the logistic regression model that can\n",
+    "# automatically tune its regularization parameter.\n",
+    "#\n",
+    "# For a fair comparison, we should run a hyper-parameter search for all the\n",
+    "# classifiers but we don't do it here for the sake of keeping the example code\n",
+    "# concise and fast to execute.\n",
+    "lr = LogisticRegressionCV(\n",
+    "    Cs=np.logspace(-6, 6, 101), cv=10, scoring=\"neg_log_loss\", max_iter=1_000\n",
+    ")\n",
+    "gnb = GaussianNB()\n",
+    "svc = NaivelyCalibratedLinearSVC(C=1.0)\n",
+    "\n",
+    "N_TREES=4000\n",
+    "MAX_FEATURES=1.0\n",
+    "rfc = RandomForestClassifier(\n",
+    "    n_estimators=N_TREES,\n",
+    "    max_features=MAX_FEATURES,\n",
+    "    random_state=42\n",
+    ")\n",
+    "hrf = HonestRandomForestClassifier(\n",
+    "    n_estimators=N_TREES,\n",
+    "    #max_samples=1.6,\n",
+    "    max_features=MAX_FEATURES,\n",
+    "    #max_features=1.0,\n",
+    "    #bootstrap=True,\n",
+    "    # stratify=True,\n",
+    "    random_state=42,\n",
+    "    honest_prior=\"ignore\",\n",
+    "    #honest_fraction=0.25\n",
+    ")\n",
+    "\n",
+    "\n",
+    "clf_list = [\n",
+    "    (lr, \"Logistic Regression\"),\n",
+    "    (gnb, \"Naive Bayes\"),\n",
+    "    (svc, \"SVC\"),\n",
+    "    (rfc, \"Random forest\"),\n",
+    "    (hrf, \"Honest Random Forest\")\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "84be4691-e90a-4159-944d-482b792f35b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 15))\n",
+    "gs = GridSpec(5, 2)\n",
+    "colors = plt.get_cmap(\"Dark2\")\n",
+    "\n",
+    "ax_calibration_curve = fig.add_subplot(gs[:2, :2])\n",
+    "calibration_displays = {}\n",
+    "markers = [\"^\", \"v\", \"s\", \"o\", \"+\"]\n",
+    "for i, (clf, name) in enumerate(clf_list):\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    display = CalibrationDisplay.from_estimator(\n",
+    "        clf,\n",
+    "        X_test,\n",
+    "        y_test,\n",
+    "        n_bins=10,\n",
+    "        name=name,\n",
+    "        ax=ax_calibration_curve,\n",
+    "        color=colors(i),\n",
+    "        marker=markers[i],\n",
+    "    )\n",
+    "    calibration_displays[name] = display\n",
+    "\n",
+    "ax_calibration_curve.grid()\n",
+    "ax_calibration_curve.set_title(\"Calibration plots\")\n",
+    "\n",
+    "# Add histogram\n",
+    "grid_positions = [(2, 0), (2, 1), (3, 0), (3, 1), (4, 0)]\n",
+    "for i, (_, name) in enumerate(clf_list):\n",
+    "    row, col = grid_positions[i]\n",
+    "    ax = fig.add_subplot(gs[row, col])\n",
+    "\n",
+    "    ax.hist(\n",
+    "        calibration_displays[name].y_prob,\n",
+    "        range=(0, 1),\n",
+    "        bins=10,\n",
+    "        label=name,\n",
+    "        color=colors(i),\n",
+    "    )\n",
+    "    ax.set(title=name, xlabel=\"Mean predicted probability\", ylabel=\"Count\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10304d43-44ac-42d5-a19f-5640e6fdb081",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/viss/test-decision-tree-zero-weights.ipynb b/examples/viss/test-decision-tree-zero-weights.ipynb
new file mode 100644
index 000000000..ad6f696a3
--- /dev/null
+++ b/examples/viss/test-decision-tree-zero-weights.ipynb
@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "224cc887-64de-49da-a04b-7d478e1fa0f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The Platonic Ideal: Verify _empirically_ that\n",
+    "# - train, test, oob mutually disjunct\n",
+    "# - train U test U oob = entire sample\n",
+    "# - all oob observations get a leaf assignment\n",
+    "# - all observations within leaf cell bounds\n",
+    "# - any way to verify optimal splits subject to constraints?\n",
+    "#\n",
+    "# The Capitulation to Reality:\n",
+    "# quite a bit of shenanigans to work around the fact that the base\n",
+    "# DecisionTreeClassifier does not retain training indices in the nodes,\n",
+    "# and therefore node membership by index cannot be verified post hoc\n",
+    "#\n",
+    "# instead we settle for the following procedure\n",
+    "# - eliminate randomness\n",
+    "# - train on untampered data to identify purported honest, structure, and oob\n",
+    "#   sample indices\n",
+    "# - shuffle y values among honest samples. if y altered y values are considered\n",
+    "#   (thereby violating honesty), the splits should change\n",
+    "# - train again from scratch on data with altered honest set\n",
+    "# - verify that splits remain the same\n",
+    "# - we only test unstratified sampling here so that we can shuffle the honest y values\n",
+    "# - we test stratified sampling at the forest level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "384dbe6e-3300-46c5-bfae-4f86c865b3df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from treeple.datasets import make_trunk_classification\n",
+    "\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "\n",
+    "N_ITER = 100\n",
+    "SAMPLE_SIZE = 256\n",
+    "RANDOM_STATE = 1\n",
+    "\n",
+    "X, y = make_trunk_classification(\n",
+    "    n_samples=SAMPLE_SIZE,\n",
+    "    n_dim=1,\n",
+    "    n_informative=1,\n",
+    "    seed=0,\n",
+    ")\n",
+    "X_t = np.concatenate((\n",
+    "    X[: SAMPLE_SIZE // 2],\n",
+    "    X[SAMPLE_SIZE // 2 :]\n",
+    "))\n",
+    "y_t = np.concatenate((np.zeros(SAMPLE_SIZE // 2), np.ones(SAMPLE_SIZE // 2)))\n",
+    "all_indices = [i for i in range(SAMPLE_SIZE)]\n",
+    "structure_indices = [i for i in range(SAMPLE_SIZE) if i % 2 == 0]\n",
+    "honest_indices = np.setdiff1d(all_indices, structure_indices)\n",
+    "w = np.ones(SAMPLE_SIZE)\n",
+    "w[honest_indices] = 0\n",
+    "\n",
+    "tree = DecisionTreeClassifier(random_state=RANDOM_STATE)\n",
+    "y_perm = y_t.ravel().copy()\n",
+    "tree.fit(X_t, y_perm, sample_weight=w)\n",
+    "old_threshold = tree.tree_.threshold.copy()\n",
+    "old_y = y_perm.copy()\n",
+    "\n",
+    "for it in range(N_ITER):\n",
+    "    tree = DecisionTreeClassifier(random_state=RANDOM_STATE)\n",
+    "    y_perm = y_t.ravel().copy()\n",
+    "    honest_shuffled = honest_indices.copy()\n",
+    "    np.random.shuffle(honest_shuffled)\n",
+    "\n",
+    "    for i in range(len(honest_indices)):\n",
+    "        y_perm[honest_indices[i]] = y_t[honest_shuffled[i]]\n",
+    "\n",
+    "    # print(f\"y_perm = {y_perm}\")\n",
+    "    assert(not np.array_equal(y_t, y_perm))\n",
+    "    assert(not np.array_equal(old_y, y_perm))\n",
+    "\n",
+    "    tree.fit(X_t, y_perm, sample_weight=w)\n",
+    "    assert(np.array_equal(old_threshold, tree.tree_.threshold))\n",
+    "    old_threshold = tree.tree_.threshold.copy()\n",
+    "\n",
+    "print(\"Done.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/viss/test-honest-forest-alter-X.ipynb b/examples/viss/test-honest-forest-alter-X.ipynb
new file mode 100644
index 000000000..1b0d2a85d
--- /dev/null
+++ b/examples/viss/test-honest-forest-alter-X.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75420844-e46a-4bb9-9879-fba35e9af2eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The Platonic Ideal: Verify empirically that\n",
+    "# - train, test, oob mutually disjunct\n",
+    "# - train U test U oob = entire sample\n",
+    "# - all oob observations get a leaf assignment\n",
+    "# - all observations within leaf cell bounds\n",
+    "# - any way to verify optimal splits subject to constraints?\n",
+    "#\n",
+    "# The Capitulation to Reality:\n",
+    "# quite a bit of shenanigans to work around the fact that the base\n",
+    "# DecisionTreeClassifier does not retain training indices in the nodes,\n",
+    "# and therefore node membership by index cannot be verified post hoc\n",
+    "#\n",
+    "# instead we settle for the following procedure\n",
+    "# - eliminate randomness\n",
+    "# - train on untampered data to identify purported honest, structure, and oob\n",
+    "#   sample indices\n",
+    "# - alter honest X values such that if they affect splits in any way,\n",
+    "#   the changes should result in different splits\n",
+    "# - verify that the splits remain the same\n",
+    "# - this tests a stronger assumption than the honesty assumption\n",
+    "#   (that honest Y values are not considered) because stratified sampling necessarily\n",
+    "#   considers Y distribution when selecting splits (for honest/structure partitioning),\n",
+    "#   so that we can't get stable partitions across trials if we alter Y values\n",
+    "# - next we alter structure X values similarly\n",
+    "# - verify that the splits change as expected\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "384dbe6e-3300-46c5-bfae-4f86c865b3df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from treeple.datasets import make_trunk_classification\n",
+    "\n",
+    "from sklearn.ensemble import HonestRandomForestClassifier as HonestForestClassifier\n",
+    "#from treeple.ensemble import HonestForestClassifier\n",
+    "\n",
+    "\n",
+    "# in order for this test to work, one must ensure that the honest split rejection\n",
+    "# criteria never veto a desired split by the shadow structure tree.\n",
+    "# the lazy way to do this is to make sure there are enough honest observations\n",
+    "# so that there will be enough on either side of any potential structure split.\n",
+    "# thus more dims => more samples\n",
+    "N_TREES = 1\n",
+    "N_DIM = 10\n",
+    "SAMPLE_SIZE = 2098\n",
+    "RANDOM_STATE = 1\n",
+    "HONEST_FRACTION = 0.95\n",
+    "STRATIFY = True\n",
+    "\n",
+    "X, y = make_trunk_classification(\n",
+    "    n_samples=SAMPLE_SIZE,\n",
+    "    n_dim=N_DIM,\n",
+    "    n_informative=1,\n",
+    "    seed=0,\n",
+    "    mu_0=-5,\n",
+    "    mu_1=5\n",
+    ")\n",
+    "X_t = np.concatenate((\n",
+    "    X[: SAMPLE_SIZE // 2],\n",
+    "    X[SAMPLE_SIZE // 2 :]\n",
+    "))\n",
+    "y_t = np.concatenate((\n",
+    "    y[: SAMPLE_SIZE // 2],\n",
+    "    y[SAMPLE_SIZE // 2 :]\n",
+    "))\n",
+    "\n",
+    "\n",
+    "def perturb(X, y, indices):\n",
+    "    for d in range(N_DIM):\n",
+    "        for i in indices:\n",
+    "            if y[i] == 0 and np.random.randint(0, 2, 1) > 0:\n",
+    "                X[i, d] -= 5\n",
+    "            elif np.random.randint(0, 2, 1) > 0:\n",
+    "                X[i, d] -= 2\n",
+    "\n",
+    "    return X, y\n",
+    "\n",
+    "\n",
+    "class Trial:\n",
+    "    def __init__(self, X, y):\n",
+    "        self.est = HonestForestClassifier(\n",
+    "            n_estimators=N_TREES,\n",
+    "            max_samples=1.0,\n",
+    "            max_features=0.3,\n",
+    "            bootstrap=True,\n",
+    "            stratify=STRATIFY,\n",
+    "            n_jobs=-2,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "            honest_prior=\"ignore\",\n",
+    "            honest_fraction=HONEST_FRACTION,\n",
+    "        )\n",
+    "        self.est.fit(X, y)\n",
+    "        \n",
+    "        self.tree = self.est.estimators_[0]\n",
+    "        self.honest_tree = self.tree.tree_\n",
+    "        self.structure_tree = self.honest_tree.target_tree\n",
+    "        self.honest_indices = np.sort(self.tree.honest_indices_)\n",
+    "        self.structure_indices = np.sort(self.tree.structure_indices_)\n",
+    "        self.threshold = self.honest_tree.target_tree.threshold.copy()\n",
+    "\n",
+    "\n",
+    "trial_results = []\n",
+    "trial_results.append(Trial(X_t, y_t))\n",
+    "\n",
+    "# perturb honest X values; threshold should not change\n",
+    "X_t, y_t = perturb(X_t, y_t, trial_results[0].honest_indices)\n",
+    "\n",
+    "trial_results.append(Trial(X_t, y_t))\n",
+    "assert np.array_equal(\n",
+    "    trial_results[0].honest_indices,\n",
+    "    trial_results[1].honest_indices\n",
+    ")\n",
+    "assert np.array_equal(\n",
+    "    trial_results[0].structure_indices,\n",
+    "    trial_results[1].structure_indices\n",
+    ")\n",
+    "assert np.array_equal(\n",
+    "    trial_results[0].threshold,\n",
+    "    trial_results[1].threshold\n",
+    "), f\"threshold1 = {trial_results[0].threshold}\\nthreshold2 = {trial_results[1].threshold}\"\n",
+    "\n",
+    "\n",
+    "# perturb structure X's; threshold should change\n",
+    "X_t, y_t = perturb(X_t, y_t, trial_results[0].structure_indices)\n",
+    "trial_results.append(Trial(X_t, y_t))\n",
+    "assert np.array_equal(\n",
+    "    trial_results[0].honest_indices,\n",
+    "    trial_results[2].honest_indices\n",
+    ")\n",
+    "assert np.array_equal(\n",
+    "    trial_results[0].structure_indices,\n",
+    "    trial_results[2].structure_indices\n",
+    ")\n",
+    "assert not np.array_equal(\n",
+    "    trial_results[0].threshold,\n",
+    "    trial_results[2].threshold\n",
+    ")\n",
+    "\n",
+    "print(\"done\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e17943e5-2dec-491c-a712-543dd5ddb9fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import StratifiedShuffleSplit\n",
+    "\n",
+    "# verify elimination of randomness from StratifiedShuffleSplit\n",
+    "ss = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=1)\n",
+    "for structure_idx, _ in ss.split(\n",
+    "    np.zeros((20, 1)), [1 if i > 10 else 0 for i in range(20)]\n",
+    "):\n",
+    "    structure_idx1 = structure_idx.copy()\n",
+    "\n",
+    "ss = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=1)\n",
+    "for structure_idx, _ in ss.split(\n",
+    "    np.zeros((20, 1)), [1 if i > 10 else 0 for i in range(20)]\n",
+    "):\n",
+    "    structure_idx2 = structure_idx.copy()\n",
+    "\n",
+    "assert np.array_equal(structure_idx1, structure_idx2)\n",
+    "\n",
+    "print(\"done\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df9b3440-ab64-41d9-904a-8cf26927a9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/viss/test-honest-tree-zero-weights.ipynb b/examples/viss/test-honest-tree-zero-weights.ipynb
new file mode 100644
index 000000000..3b550f647
--- /dev/null
+++ b/examples/viss/test-honest-tree-zero-weights.ipynb
@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "75420844-e46a-4bb9-9879-fba35e9af2eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The Platonic Ideal: Verify _empirically_ that\n",
+    "# - train, test, oob mutually disjunct\n",
+    "# - train U test U oob = entire sample\n",
+    "# - all oob observations get a leaf assignment\n",
+    "# - all observations within leaf cell bounds\n",
+    "# - any way to verify optimal splits subject to constraints?\n",
+    "#\n",
+    "# The Capitulation to Reality:\n",
+    "# quite a bit of shenanigans to work around the fact that the base\n",
+    "# DecisionTreeClassifier does not retain training indices in the nodes,\n",
+    "# and therefore node membership by index cannot be verified post hoc\n",
+    "#\n",
+    "# instead we settle for the following procedure\n",
+    "# - eliminate randomness\n",
+    "# - train on untampered data to identify purported honest, structure, and oob\n",
+    "#   sample indices\n",
+    "# - shuffle y values among honest samples. if y altered y values are considered\n",
+    "#   (thereby violating honesty), the splits should change\n",
+    "# - train again from scratch on data with altered honest set\n",
+    "# - verify that splits remain the same\n",
+    "# - we only test unstratified sampling here so that we can shuffle the honest y values\n",
+    "# - we test stratified sampling at the forest level\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "384dbe6e-3300-46c5-bfae-4f86c865b3df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from treeple.datasets import make_trunk_classification\n",
+    "\n",
+    "from sklearn.tree import DecisionTreeClassifier, HonestDecisionTree\n",
+    "#from treeple.ensemble import HonestForestClassifier\n",
+    "\n",
+    "\n",
+    "N_ITER = 100\n",
+    "SAMPLE_SIZE = 1024\n",
+    "RANDOM_STATE = 1\n",
+    "HONEST_PRIOR = \"ignore\"\n",
+    "HONEST_FRACTION = 0.9\n",
+    "\n",
+    "X, y = make_trunk_classification(\n",
+    "    n_samples=SAMPLE_SIZE,\n",
+    "    n_dim=1,\n",
+    "    n_informative=1,\n",
+    "    seed=0,\n",
+    ")\n",
+    "X_t = np.concatenate((\n",
+    "    X[: SAMPLE_SIZE // 2],\n",
+    "    X[SAMPLE_SIZE // 2 :]\n",
+    "))\n",
+    "y_t = np.concatenate((np.zeros(SAMPLE_SIZE // 2), np.ones(SAMPLE_SIZE // 2)))\n",
+    "\n",
+    "\n",
+    "tree=HonestDecisionTree(\n",
+    "    target_tree_class=DecisionTreeClassifier,\n",
+    "    target_tree_kwargs={\n",
+    "        \"criterion\": \"gini\",\n",
+    "        \"random_state\": RANDOM_STATE\n",
+    "    },\n",
+    "    honest_prior=HONEST_PRIOR,\n",
+    "    honest_fraction=HONEST_FRACTION\n",
+    ")\n",
+    "tree.fit(X_t, y_t.ravel())\n",
+    "honest_tree = tree.tree_\n",
+    "structure_tree = honest_tree.target_tree\n",
+    "old_threshold = structure_tree.threshold.copy()\n",
+    "old_y = y_t.copy()\n",
+    "\n",
+    "honest_indices = tree.honest_indices_\n",
+    "\n",
+    "for _ in range(N_ITER):\n",
+    "    y_perm = y_t.copy()\n",
+    "    honest_shuffled = honest_indices.copy()\n",
+    "    np.random.shuffle(honest_shuffled)\n",
+    "    for i in range(len(honest_indices)):\n",
+    "        y_perm[honest_indices[i]] = y_t[honest_shuffled[i]]\n",
+    "    \n",
+    "    assert(not np.array_equal(y_t, y_perm))\n",
+    "    assert(not np.array_equal(old_y, y_perm))\n",
+    "\n",
+    "    tree=HonestDecisionTree(\n",
+    "        target_tree_class=DecisionTreeClassifier,\n",
+    "        target_tree_kwargs={\n",
+    "            \"criterion\": \"gini\",\n",
+    "            \"random_state\": RANDOM_STATE\n",
+    "        },\n",
+    "        honest_prior=HONEST_PRIOR,\n",
+    "        honest_fraction=HONEST_FRACTION\n",
+    "    )\n",
+    "    tree.fit(X_t, y_perm.ravel())\n",
+    "    honest_tree = tree.tree_\n",
+    "    structure_tree = honest_tree.target_tree\n",
+    "\n",
+    "    assert(np.array_equal(old_threshold, structure_tree.threshold))\n",
+    "    old_threshold = structure_tree.threshold.copy()\n",
+    "    old_y = y_perm.copy()\n",
+    "\n",
+    "print(\"done\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e17943e5-2dec-491c-a712-543dd5ddb9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/meson.build b/meson.build
index 07ec4c9c2..51acaf00d 100644
--- a/meson.build
+++ b/meson.build
@@ -61,6 +61,7 @@ endif
 
 # r = run_command('git', 'submodule', 'update', '--init', check: false)
 r = run_command('mv', 'treeple/_lib/sklearn_fork/sklearn', 'treeple/_lib/sklearn', check: false)
+r = run_command('mv', 'treeple/_lib_experimental/sklearn_fork/sklearn', 'treeple/_lib_experimental/sklearn', check: false)
 
 # Setup Python:
 # https://mesonbuild.com/Python-module.html
diff --git a/pyproject.toml b/pyproject.toml
index c0a50d95a..bca2bac83 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -129,7 +129,7 @@ extra = [
 ]
 
 [tool.bandit]
-exclude_dirs = ["treeple/tests", "treeple/**/tests/*", 'treeple/_build_utils/*', 'treeple/_lib/*']
+exclude_dirs = ["treeple/tests", "treeple/**/tests/*", 'treeple/_build_utils/*', 'treeple/_lib/*', 'treeple/_lib_experimental/*']
 skips = ['B404', 'B603']
 
 [tool.black]
@@ -141,6 +141,7 @@ extend-exclude = '''
       __pycache__
     | \.github
     | treeple/_lib
+    | treeple/_lib_experimental
     | .asv
     | env
     | build-install
@@ -150,7 +151,7 @@ extend-exclude = '''
 [tool.codespell]
 builtin = "clear,rare,informal,names,usage"
 ignore-words = ".codespellignore"
-skip = "doc/references.bib,treeple/_lib/"
+skip = "doc/references.bib,treeple/_lib/,treeple/_lib_experimental/"
 
 [tool.coverage.report]
 exclude_lines = ['pragma: no cover', 'if __name__ == .__main__.:']
@@ -160,7 +161,7 @@ precision = 2
 branch = true
 cover_pylib = false
 source = ['treeple']
-omit = ['**/__init__.py', '**/tests/**', 'treeple/_build_utils/*', 'treeple/_lib/*']
+omit = ['**/__init__.py', '**/tests/**', 'treeple/_build_utils/*', 'treeple/_lib/*', 'treeple/_lib_experimental/*']
 
 [tool.cython-lint]
 # Ignore the same error codes as flake8
@@ -188,26 +189,27 @@ profile = 'black'
 multi_line_output = 3
 line_length = 100
 py_version = 38
-extend_skip_glob = ['treeple/__init__.py', 'treeple/_lib/*', '.asv/*', 'env/*', 'build-install/*']
+extend_skip_glob = ['treeple/__init__.py', 'treeple/_lib/*', 'treeple/_lib_experimental/*', '.asv/*', 'env/*', 'build-install/*']
 
 [tool.mypy]
 ignore_missing_imports = true
 no_site_packages = true
 exclude = [
   'treeple/_lib/',
+  'treeple/_lib_experimental/',
   'benchmarks_nonasv/'
 ]
 
 [tool.pydocstyle]
 convention = 'numpy'
 ignore-decorators = '(copy_doc|property|.*setter|.*getter)'
-match = '^(?!setup|__init__|test_|_lib).*\.py'
+match = '^(?!setup|__init__|test_|_lib|_lib_experimental).*\.py'
 match-dir = '^treeple*'
 add_ignore = 'D100,D104,D105,D107'
 
 [tool.pytest.ini_options]
 minversion = '6.0'
-addopts = '--durations 20 --junit-xml=junit-results.xml --verbose --ignore=treeple/_lib/ -k "not slowtest"'
+addopts = '--durations 20 --junit-xml=junit-results.xml --verbose --ignore=treeple/_lib/ --ignore=treeple/_lib_experimental/ -k "not slowtest"'
 filterwarnings = [
   'ignore:Using sklearn tree so store_leaf_values cannot be set.*'
 ]
diff --git a/treeple/_lib_experimental/__init__.py b/treeple/_lib_experimental/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/treeple/_lib_experimental/sklearn_fork b/treeple/_lib_experimental/sklearn_fork
new file mode 160000
index 000000000..7059bf7e8
--- /dev/null
+++ b/treeple/_lib_experimental/sklearn_fork
@@ -0,0 +1 @@
+Subproject commit 7059bf7e81a1dacfe656a3ecc421f95df288a890
diff --git a/treeple/honesty/Readme.md b/treeple/honesty/Readme.md
new file mode 100644
index 000000000..324c10193
--- /dev/null
+++ b/treeple/honesty/Readme.md
@@ -0,0 +1,54 @@
+# Honesty
+
+## Use
+There is now a submodule called `_lib_experimental/sklearn_fork`, parallel to `_lib/sklearn_fork`. Build, install, and import it as an editable install using the same procedure you use with the main `neurodata` fork submodule. This of course means you must either pick one or the other at a time, or import them under different aliases.
+
+For examples of `HonestRandomForestClassifier` use, see the test functions `test_honest_forest_separation` and `test_honest_forest_iris_criterion` in `_lib_experimental/sklearn_fork/sklearn/ensemble/tests/test_forest.py`.
+
+
+## `scikit-learn`
+### Architectural Changes
+Adding honesty to `scikit-learn` in a sensible way required adding some architectural elements to the existing `scikit-learn` tree implementation. In particular, in order to make honesty a module composable into arbitrary types of trees, I added:
+1. Added an injectable split rejection criteria pattern to `Splitter` so that arbitrary lists of split rejection criteria could be specified at runtime, and execute with no perceivable marginal overhead.
+2. A lightweight event broker and handler framework to `TreeBuilder` so that interested parties can observe tree build status. In particular, this feature is used by the honesty module to grow a shadow tree which partitions the honest set using structure tree splits, and has veto power over structure tree candidate splits using the aforementioned injectable split rejection criteria.
+
+There are more technically detailed comments in the files themselves.
+
+
+### Summary of File Changes
+- `ensemble/forest.py`
+    - Added `HonestRandomForestClassifier`.
+- `ensemble/tests/test_forest.py`
+    - Added some unit tests for `HonestRandomForestClassifier`.
+- `tree/_classes.py`
+    - Refactored `BaseDecisionTree._fit` to separate all the data prep from the call to `BaseDecisionTree._build_tree`.
+    - Added a data-bearing class `BuildTreeArgs` to carry all the post-`_prep_data` state information around.
+    - Moved creation of `Criterion` from `BaseDecisionTree._build_tree` to an overridable factory method called from `BaseDecisionTree._fit`, so that `Criterion` is passed into `BaseDecisionTree._build_tree` as a parameter.
+    - The idea is that committing to an implementation of `Criterion` should be hoisted as "high" as possible, but as currently implemented instantiation depends on class distribution analysis done in `BaseDecisionTree._prep_data`.
+- `tree/_events.{pxd, pyx}`
+    - Added for event broker/handler implementation.
+- `tree/_honest_tree.py`
+    - Honest classification tree implementation.
+- `tree/_honesty.{pxd, pyx}`
+    - Honesty module implementation.
+- `tree/_partitioner.{pxd, pyx}`
+    - Added to break `Partitioner` out of `Splitter` module for clearer reuse.
+    - Also refactored the existing `{Dense, Sparse}Partitioner` fused type design to prevent the proliferation of concrete container classes required by that design.
+- `tree/_sort.{pxd, pyx}`
+    - Added to break out of `Splitter` module since these functions are used by both `Splitter` and `Partitioner`, and we want to avoid cyclic dependencies.
+- `tree/_splitter.{pxd, pyx}`
+    - Updated to introduce injectable split rejection criteria.
+    - Refactored a bit to accommodate factory creation of extended `SplitRecord` types, as used by obliqueness in `treeple`.
+- `tree/_test.{pxd, pyx}`
+    - Added to implement some necessary cython test functionality for honesty.
+- `tree/_tree.{pxd, pyx}`
+    - Updated to introduce event firing to tree build process.
+    - Refactored to break out a large block of duplicate code in `TreeBuilder.build` from the `neurodata` fork into its own method `TreeBuilder._build_body`, invoked from `TreeBuilder.build`.
+- `tree/tests/test_tree.py`
+    - Added some unit tests for honesty.
+
+### Suggested Future Work
+The `tree` package in `scikit-learn` is overdue for a good deal of refactoring. I would do it in multiple passes, at least the following:
+- Eliminate all the introspection high up in the inheritance hierarchy. An obvious example is in `BaseDecisionTree._prep_data` (formerly in `BaseDecisionTree._fit`), where the function asks its containing class "am I a classifier?" This antipattern of switching behavior which requires foreknowledge of future structure occurs throughout the existing codebase, and makes it impossible to extend and reuse existing code without either modifying it or duplicating a great deal of it. In the case of `is_classifier(self)`, stop calling it from functions in `BaseDecisionTree`, and push that switched functionality down into actual classifiers where it belongs.
+- Hoist commitments to particular implementations of interfaces. For instance, the aforementioned `Criterion` selection logic buried in the implementation of tree build. Parameterize these things, and defer commitment as "late" as possible, all the way back to the runtime interface presented to the user if possible.
+- Be more judicious with inheritance, and more prolific with composition. Functionality inserted at some point in the inheritance hierarchy cannot easily be reused in sibling branches; if you want the functionality, you have to inherit, forcing your new code into a corner of the hierarchy where it doesn't necessarily belong. An obvious place to begin unwinding this in the current codebase is to merge upstream the defused `Partitioner` refactor that I did, and eliminate all the classes that were forced to exist by the fused `Partitioner`, deferring selection of concrete implementation to runtime and passing it in as a parameter value.
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