.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_decision_tree_logreg.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_decision_tree_logreg.py: Decision Tree and Logistic Regression ===================================== The notebook demonstrates the model *DecisionTreeLogisticRegression* which replaces the decision based on one variable by a logistic regression. Iris dataset and logistic regression ------------------------------------ The following code shows the border defined by two machine learning models on the `Iris dataset `_. .. GENERATED FROM PYTHON SOURCE LINES 16-57 .. code-block:: Python import numpy from scipy.spatial.distance import cdist import matplotlib.pyplot as plt from pandas import DataFrame from tqdm import tqdm from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from mlinsights.mlmodel import DecisionTreeLogisticRegression from mlinsights.mltree import predict_leaves def plot_classifier_decision_zone(clf, X, y, title=None, ax=None): if ax is None: ax = plt.gca() x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 dhx = (x_max - x_min) / 100 dhy = (y_max - y_min) / 100 xx, yy = numpy.meshgrid( numpy.arange(x_min, x_max, dhx), numpy.arange(y_min, y_max, dhy) ) Z = clf.predict(numpy.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) ax.contourf(xx, yy, Z, alpha=0.5) ax.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k", lw=0.5) if title is not None: ax.set_title(title) iris = load_iris() X = iris.data[:, [0, 2]] y = iris.target y = y % 2 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.6, shuffle=True) .. GENERATED FROM PYTHON SOURCE LINES 59-63 .. code-block:: Python lr = LogisticRegression() lr.fit(X_train, y_train) .. raw:: html

LogisticRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

.. GENERATED FROM PYTHON SOURCE LINES 65-69 .. code-block:: Python dt = DecisionTreeClassifier(criterion="entropy") dt.fit(X_train, y_train) .. raw:: html

DecisionTreeClassifier(criterion='entropy')

.. GENERATED FROM PYTHON SOURCE LINES 71-79 .. code-block:: Python fig, ax = plt.subplots(1, 2, figsize=(10, 4)) plot_classifier_decision_zone(lr, X_test, y_test, ax=ax[0], title="LogisticRegression") plot_classifier_decision_zone( dt, X_test, y_test, ax=ax[1], title="DecisionTreeClassifier" ) .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_001.png :alt: LogisticRegression, DecisionTreeClassifier :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 80-82 The logistic regression is not very stable on this sort of problem. No linear separator can work on this dataset. Let's dig into it. .. GENERATED FROM PYTHON SOURCE LINES 85-87 DecisionTreeLogisticRegression ------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 87-98 .. code-block:: Python dtlr = DecisionTreeLogisticRegression( estimator=LogisticRegression(solver="liblinear"), min_samples_leaf=10, min_samples_split=10, max_depth=1, fit_improve_algo="none", ) dtlr.fit(X_train, y_train) .. raw:: html

DecisionTreeLogisticRegression(estimator=LogisticRegression(solver='liblinear'),
                                   fit_improve_algo='none', max_depth=1,
                                   min_samples_leaf=10, min_samples_split=10)

.. GENERATED FROM PYTHON SOURCE LINES 100-142 .. code-block:: Python dtlr2 = DecisionTreeLogisticRegression( estimator=LogisticRegression(solver="liblinear"), min_samples_leaf=4, min_samples_split=4, max_depth=10, fit_improve_algo="intercept_sort_always", ) dtlr2.fit(X_train, y_train) fig, ax = plt.subplots(2, 2, figsize=(10, 8)) plot_classifier_decision_zone( dtlr, X_train, y_train, ax=ax[0, 0], title="DecisionTreeLogisticRegression\ndepth=%d - train" % dtlr.tree_depth_, ) plot_classifier_decision_zone( dtlr2, X_train, y_train, ax=ax[0, 1], title="DecisionTreeLogisticRegression\ndepth=%d - train" % dtlr2.tree_depth_, ) plot_classifier_decision_zone( dtlr, X_test, y_test, ax=ax[1, 0], title="DecisionTreeLogisticRegression\ndepth=%d - test" % dtlr.tree_depth_, ) plot_classifier_decision_zone( dtlr2, X_test, y_test, ax=ax[1, 1], title="DecisionTreeLogisticRegression\ndepth=%d - test" % dtlr2.tree_depth_, ) .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_002.png :alt: DecisionTreeLogisticRegression depth=1 - train, DecisionTreeLogisticRegression depth=4 - train, DecisionTreeLogisticRegression depth=1 - test, DecisionTreeLogisticRegression depth=4 - test :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 144-157 .. code-block:: Python rows = [] for model in [lr, dt, dtlr, dtlr2]: val = (" - depth=%d" % model.tree_depth_) if hasattr(model, "tree_depth_") else "" obs = dict( name="%s%s" % (model.__class__.__name__, val), score=model.score(X_test, y_test) ) rows.append(obs) DataFrame(rows) .. raw:: html

	name	score
0	LogisticRegression	0.633333
1	DecisionTreeClassifier	0.933333
2	DecisionTreeLogisticRegression - depth=1	0.677778
3	DecisionTreeLogisticRegression - depth=4	0.911111

.. GENERATED FROM PYTHON SOURCE LINES 158-160 A first example --------------- .. GENERATED FROM PYTHON SOURCE LINES 160-175 .. code-block:: Python def random_set_simple(n): X = numpy.random.rand(n, 2) y = ((X[:, 0] ** 2 + X[:, 1] ** 2) <= 1).astype(numpy.int32).ravel() return X, y X, y = random_set_simple(2000) X_train, X_test, y_train, y_test = train_test_split(X, y) dt = DecisionTreeClassifier(max_depth=3) dt.fit(X_train, y_train) dt8 = DecisionTreeClassifier(max_depth=10) dt8.fit(X_train, y_train) .. raw:: html

DecisionTreeClassifier(max_depth=10)

.. GENERATED FROM PYTHON SOURCE LINES 177-230 .. code-block:: Python fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharey=True) plot_classifier_decision_zone( dt, X_test, y_test, ax=ax[0], title="DecisionTree - max_depth=%d\nacc=%1.2f" % (dt.max_depth, dt.score(X_test, y_test)), ) plot_classifier_decision_zone( dt8, X_test, y_test, ax=ax[1], title="DecisionTree - max_depth=%d\nacc=%1.2f" % (dt8.max_depth, dt8.score(X_test, y_test)), ) ax[0].set_xlim([0, 1]) ax[1].set_xlim([0, 1]) ax[0].set_ylim([0, 1]) dtlr = DecisionTreeLogisticRegression( max_depth=3, fit_improve_algo="intercept_sort_always", verbose=1 ) dtlr.fit(X_train, y_train) dtlr8 = DecisionTreeLogisticRegression( max_depth=10, min_samples_split=4, fit_improve_algo="intercept_sort_always" ) dtlr8.fit(X_train, y_train) fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharey=True) plot_classifier_decision_zone( dtlr, X_test, y_test, ax=ax[0], title="DecisionTreeLogReg - depth=%d\nacc=%1.2f" % (dtlr.tree_depth_, dtlr.score(X_test, y_test)), ) plot_classifier_decision_zone( dtlr8, X_test, y_test, ax=ax[1], title="DecisionTreeLogReg - depth=%d\nacc=%1.2f" % (dtlr8.tree_depth_, dtlr8.score(X_test, y_test)), ) ax[0].set_xlim([0, 1]) ax[1].set_xlim([0, 1]) ax[0].set_ylim([0, 1]) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_003.png :alt: DecisionTree - max_depth=3 acc=0.95, DecisionTree - max_depth=10 acc=0.99 :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_004.png :alt: DecisionTreeLogReg - depth=3 acc=0.84, DecisionTreeLogReg - depth=10 acc=0.99 :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none [DTLR ] trained acc 0.96 N=1500 [DTLRI] change intercept 11.339838 --> 10.479621 in [0.353608, 16.298707] [DTLR*] above: n_class=2 N=1500 - 1095/1500 [DTLR ] trained acc 0.98 N=1095 [DTLRI] change intercept 5.941792 --> 2.141152 in [0.090548, 3.113309] [DTLR*] above: n_class=2 N=1095 - 765/1500 [DTLR ] trained acc 0.99 N=765 [DTLRI] change intercept 5.938701 --> 1.801736 in [-0.234086, 2.286637] [DTLR*] below: n_class=2 N=1095 - 330/1500 [DTLR ] trained acc 0.96 N=330 [DTLRI] change intercept 4.766387 --> 1.753661 in [0.157104, 2.312669] [DTLR*] below: n_class=2 N=1500 - 405/1500 [DTLR ] trained acc 0.76 N=405 [DTLRI] change intercept 5.661565 --> 6.622864 in [5.642996, 9.176333] [DTLR*] above: n_class=2 N=405 - 163/1500 [DTLR ] trained acc 0.59 N=163 [DTLRI] change intercept 1.717183 --> 1.362206 in [1.140047, 1.557262] [DTLR*] below: n_class=1 N=405 - 242/1500 (0.0, 1.0) .. GENERATED FROM PYTHON SOURCE LINES 232-281 .. code-block:: Python def draw_border( clr, X, y, fct=None, incx=0.1, incy=0.1, figsize=None, border=True, ax=None, s=10.0, linewidths=0.1, ): h = 0.02 x_min, x_max = X[:, 0].min() - incx, X[:, 0].max() + incx y_min, y_max = X[:, 1].min() - incy, X[:, 1].max() + incy xx, yy = numpy.meshgrid( numpy.arange(x_min, x_max, h), numpy.arange(y_min, y_max, h) ) if fct is None: Z = clr.predict(numpy.c_[xx.ravel(), yy.ravel()]) else: Z = fct(clr, numpy.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot cmap = plt.cm.tab20 Z = Z.reshape(xx.shape) if ax is None: fig, ax = plt.subplots(1, 1, figsize=figsize or (4, 3)) ax.pcolormesh(xx, yy, Z, cmap=cmap) # Plot also the training points ax.scatter( X[:, 0], X[:, 1], c=y, edgecolors="k", cmap=cmap, s=s, linewidths=linewidths ) ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) return ax fig, ax = plt.subplots(1, 2, figsize=(14, 4)) draw_border(dt, X_test, y_test, border=False, ax=ax[0]) ax[0].set_title("Iris") draw_border(dt, X, y, border=False, ax=ax[1], fct=lambda m, x: predict_leaves(m, x)) ax[1].set_title("DecisionTree") .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_005.png :alt: Iris, DecisionTree :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_005.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(0.5, 1.0, 'DecisionTree') .. GENERATED FROM PYTHON SOURCE LINES 283-329 .. code-block:: Python fig, ax = plt.subplots(6, 4, figsize=(12, 16)) for i, depth in tqdm(enumerate((1, 2, 3, 4, 5, 6))): dtl = DecisionTreeLogisticRegression( max_depth=depth, fit_improve_algo="intercept_sort_always", min_samples_leaf=2 ) dtl.fit(X_train, y_train) draw_border(dtl, X_test, y_test, border=False, ax=ax[i, 0], s=4.0) draw_border( dtl, X, y, border=False, ax=ax[i, 1], fct=lambda m, x: predict_leaves(m, x), s=4.0, ) ax[i, 0].set_title( "Depth=%d nodes=%d score=%1.2f" % (dtl.tree_depth_, dtl.n_nodes_, dtl.score(X_test, y_test)) ) ax[i, 1].set_title("DTLR Leaves zones") dtl = DecisionTreeClassifier(max_depth=depth) dtl.fit(X_train, y_train) draw_border(dtl, X_test, y_test, border=False, ax=ax[i, 2], s=4.0) draw_border( dtl, X, y, border=False, ax=ax[i, 3], fct=lambda m, x: predict_leaves(m, x), s=4.0, ) ax[i, 2].set_title( "Depth=%d nodes=%d score=%1.2f" % (dtl.max_depth, dtl.tree_.node_count, dtl.score(X_test, y_test)) ) ax[i, 3].set_title("DT Leaves zones") for k in range(ax.shape[1]): ax[i, k].get_xaxis().set_visible(False) .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_006.png :alt: Depth=1 nodes=1 score=0.93, DTLR Leaves zones, Depth=1 nodes=3 score=0.81, DT Leaves zones, Depth=2 nodes=3 score=0.73, DTLR Leaves zones, Depth=2 nodes=7 score=0.88, DT Leaves zones, Depth=3 nodes=7 score=0.84, DTLR Leaves zones, Depth=3 nodes=15 score=0.95, DT Leaves zones, Depth=4 nodes=13 score=0.92, DTLR Leaves zones, Depth=4 nodes=29 score=0.96, DT Leaves zones, Depth=5 nodes=21 score=0.95, DTLR Leaves zones, Depth=5 nodes=47 score=0.99, DT Leaves zones, Depth=6 nodes=33 score=0.96, DTLR Leaves zones, Depth=6 nodes=61 score=0.99, DT Leaves zones :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 0it [00:00, ?it/s] 1it [00:00, 8.84it/s] 2it [00:00, 6.81it/s] 3it [00:00, 5.75it/s] 4it [00:00, 5.54it/s] 5it [00:00, 5.18it/s] 6it [00:01, 4.77it/s] 6it [00:01, 5.27it/s] .. GENERATED FROM PYTHON SOURCE LINES 330-334 Another example designed to fail -------------------------------- Designed to be difficult with a regular decision tree. .. GENERATED FROM PYTHON SOURCE LINES 334-375 .. code-block:: Python def random_set(n): X = numpy.random.rand(n, 2) y = ( (cdist(X, numpy.array([[0.5, 0.5]]), metric="minkowski", p=1) <= 0.5) .astype(numpy.int32) .ravel() ) return X, y X, y = random_set(2000) X_train, X_test, y_train, y_test = train_test_split(X, y) dt = DecisionTreeClassifier(max_depth=3) dt.fit(X_train, y_train) dt8 = DecisionTreeClassifier(max_depth=10) dt8.fit(X_train, y_train) fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharey=True) plot_classifier_decision_zone( dt, X_test, y_test, ax=ax[0], title="DecisionTree - max_depth=%d\nacc=%1.2f" % (dt.max_depth, dt.score(X_test, y_test)), ) plot_classifier_decision_zone( dt8, X_test, y_test, ax=ax[1], title="DecisionTree - max_depth=%d\nacc=%1.2f" % (dt8.max_depth, dt8.score(X_test, y_test)), ) ax[0].set_xlim([0, 1]) ax[1].set_xlim([0, 1]) ax[0].set_ylim([0, 1]) .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_007.png :alt: DecisionTree - max_depth=3 acc=0.79, DecisionTree - max_depth=10 acc=0.96 :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_007.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (0.0, 1.0) .. GENERATED FROM PYTHON SOURCE LINES 376-379 The example is a square rotated by 45 degrees. Every sample in the square is a positive sample, every sample outside is a negative one. The tree approximates the border with horizontal and vertical lines. .. GENERATED FROM PYTHON SOURCE LINES 379-412 .. code-block:: Python dtlr = DecisionTreeLogisticRegression( max_depth=3, fit_improve_algo="intercept_sort_always", verbose=1 ) dtlr.fit(X_train, y_train) dtlr8 = DecisionTreeLogisticRegression( max_depth=10, min_samples_split=4, fit_improve_algo="intercept_sort_always" ) dtlr8.fit(X_train, y_train) fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharey=True) plot_classifier_decision_zone( dtlr, X_test, y_test, ax=ax[0], title="DecisionTreeLogReg - depth=%d\nacc=%1.2f" % (dtlr.tree_depth_, dtlr.score(X_test, y_test)), ) plot_classifier_decision_zone( dtlr8, X_test, y_test, ax=ax[1], title="DecisionTreeLogReg - depth=%d\nacc=%1.2f" % (dtlr8.tree_depth_, dtlr8.score(X_test, y_test)), ) ax[0].set_xlim([0, 1]) ax[1].set_xlim([0, 1]) ax[0].set_ylim([0, 1]) .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_008.png :alt: DecisionTreeLogReg - depth=3 acc=0.56, DecisionTreeLogReg - depth=10 acc=0.95 :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_008.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [DTLR ] trained acc 0.46 N=1500 [DTLRI] change intercept 0.031077 --> 0.047175 in [-0.061557, 0.162851] [DTLR*] above: n_class=2 N=1500 - 749/1500 [DTLR ] trained acc 0.74 N=749 [DTLRI] change intercept -1.090538 --> -1.104718 in [-3.351505, 0.757671] [DTLR*] above: n_class=2 N=749 - 380/1500 [DTLR ] trained acc 0.75 N=380 [DTLRI] change intercept 0.040429 --> -1.003865 in [-1.780948, -0.464175] [DTLR*] below: n_class=2 N=749 - 369/1500 [DTLR ] trained acc 0.72 N=369 [DTLRI] change intercept -0.850604 --> 0.087702 in [-0.461257, 0.769244] [DTLR*] below: n_class=2 N=1500 - 751/1500 [DTLR ] trained acc 0.72 N=751 [DTLRI] change intercept 3.253194 --> 3.352934 in [1.485724, 4.947384] [DTLR*] above: n_class=2 N=751 - 367/1500 [DTLR ] trained acc 0.71 N=367 [DTLRI] change intercept 0.952998 --> 0.042261 in [-0.294482, 0.408440] [DTLR*] below: n_class=2 N=751 - 384/1500 [DTLR ] trained acc 0.76 N=384 [DTLRI] change intercept 0.941037 --> 2.034576 in [1.496790, 2.717028] (0.0, 1.0) .. GENERATED FROM PYTHON SOURCE LINES 413-415 Leave zones ----------- .. GENERATED FROM PYTHON SOURCE LINES 415-426 .. code-block:: Python # We use method *decision_path* to understand which leaf is responsible # for which zone. fig, ax = plt.subplots(1, 2, figsize=(14, 4)) draw_border(dtlr, X_test, y_test, border=False, ax=ax[0]) ax[0].set_title("Iris") draw_border(dtlr, X, y, border=False, ax=ax[1], fct=lambda m, x: predict_leaves(m, x)) ax[1].set_title("DecisionTreeLogisticRegression") .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_009.png :alt: Iris, DecisionTreeLogisticRegression :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_009.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(0.5, 1.0, 'DecisionTreeLogisticRegression') .. GENERATED FROM PYTHON SOURCE LINES 428-469 .. code-block:: Python fig, ax = plt.subplots(6, 4, figsize=(12, 16)) for i, depth in tqdm(enumerate((1, 2, 3, 4, 5, 6))): dtl = DecisionTreeLogisticRegression( max_depth=depth, fit_improve_algo="intercept_sort_always", min_samples_leaf=2 ) dtl.fit(X_train, y_train) draw_border(dtl, X_test, y_test, border=False, ax=ax[i, 0], s=4.0) draw_border( dtl, X, y, border=False, ax=ax[i, 1], fct=lambda m, x: predict_leaves(m, x), s=4.0, ) ax[i, 0].set_title( "Depth=%d nodes=%d score=%1.2f" % (dtl.tree_depth_, dtl.n_nodes_, dtl.score(X_test, y_test)) ) ax[i, 1].set_title("DTLR Leaves zones") dtl = DecisionTreeClassifier(max_depth=depth) dtl.fit(X_train, y_train) draw_border(dtl, X_test, y_test, border=False, ax=ax[i, 2], s=4.0) draw_border( dtl, X, y, border=False, ax=ax[i, 3], fct=lambda m, x: predict_leaves(m, x), s=4.0, ) ax[i, 2].set_title( "Depth=%d nodes=%d score=%1.2f" % (dtl.max_depth, dtl.tree_.node_count, dtl.score(X_test, y_test)) ) ax[i, 3].set_title("DT Leaves zones") .. image-sg:: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_010.png :alt: Depth=1 nodes=1 score=0.54, DTLR Leaves zones, Depth=1 nodes=3 score=0.67, DT Leaves zones, Depth=2 nodes=3 score=0.75, DTLR Leaves zones, Depth=2 nodes=7 score=0.74, DT Leaves zones, Depth=3 nodes=7 score=0.56, DTLR Leaves zones, Depth=3 nodes=15 score=0.79, DT Leaves zones, Depth=4 nodes=15 score=0.80, DTLR Leaves zones, Depth=4 nodes=29 score=0.86, DT Leaves zones, Depth=5 nodes=31 score=0.79, DTLR Leaves zones, Depth=5 nodes=49 score=0.90, DT Leaves zones, Depth=6 nodes=59 score=0.84, DTLR Leaves zones, Depth=6 nodes=73 score=0.92, DT Leaves zones :srcset: /auto_examples/images/sphx_glr_plot_decision_tree_logreg_010.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 0it [00:00, ?it/s] 2it [00:00, 10.45it/s] 4it [00:00, 6.72it/s] 5it [00:00, 5.57it/s] 6it [00:01, 4.72it/s] 6it [00:01, 5.43it/s] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.612 seconds) .. _sphx_glr_download_auto_examples_plot_decision_tree_logreg.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_decision_tree_logreg.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_decision_tree_logreg.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_decision_tree_logreg.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_