Pushing the docs to dev/ for branch: main, commit 325930e0151b67f6ddcd155d47a7b2a320f30ea7

Author: sklearn-ci

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -1,46 +1,62 @@
 """
-===================================================================
+========================
 Decision Tree Regression
-===================================================================
-
-A 1D regression with decision tree.
-
-The :ref:`decision trees <tree>` is
-used to fit a sine curve with addition noisy observation. As a result, it
-learns local linear regressions approximating the sine curve.
-
-We can see that if the maximum depth of the tree (controlled by the
-`max_depth` parameter) is set too high, the decision trees learn too fine
-details of the training data and learn from the noise, i.e. they overfit.
+========================
+In this example, we demonstrate the effect of changing the maximum depth of a
+decision tree on how it fits to the data. We perform this once on a 1D regression
+task and once on a multi-output regression task.
 """
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-# Import the necessary modules and libraries
-import matplotlib.pyplot as plt
+# %%
+# Decision Tree on a 1D Regression Task
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Here we fit a tree on a 1D regression task.
+#
+# The :ref:`decision trees <tree>` is
+# used to fit a sine curve with addition noisy observation. As a result, it
+# learns local linear regressions approximating the sine curve.
+#
+# We can see that if the maximum depth of the tree (controlled by the
+# `max_depth` parameter) is set too high, the decision trees learn too fine
+# details of the training data and learn from the noise, i.e. they overfit.
+#
+# Create a random 1D dataset
+# --------------------------
 import numpy as np
 
-from sklearn.tree import DecisionTreeRegressor
-
-# Create a random dataset
 rng = np.random.RandomState(1)
 X = np.sort(5 * rng.rand(80, 1), axis=0)
 y = np.sin(X).ravel()
 y[::5] += 3 * (0.5 - rng.rand(16))
 
+# %%
 # Fit regression model
+# --------------------
+# Here we fit two models with different maximum depths
+from sklearn.tree import DecisionTreeRegressor
+
 regr_1 = DecisionTreeRegressor(max_depth=2)
 regr_2 = DecisionTreeRegressor(max_depth=5)
 regr_1.fit(X, y)
 regr_2.fit(X, y)
 
+# %%
 # Predict
+# -------
+# Get predictions on the test set
 X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
 y_1 = regr_1.predict(X_test)
 y_2 = regr_2.predict(X_test)
 
+# %%
 # Plot the results
+# ----------------
+import matplotlib.pyplot as plt
+
 plt.figure()
 plt.scatter(X, y, s=20, edgecolor="black", c="darkorange", label="data")
 plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
@@ -50,3 +66,79 @@
 plt.title("Decision Tree Regression")
 plt.legend()
 plt.show()
+
+# %%
+# As you can see, the model with a depth of 5 (yellow) learns the details of the
+# training data to the point that it overfits to the noise. On the other hand,
+# the model with a depth of 2 (blue) learns the major tendencies in the data well
+# and does not overfit. In real use cases, you need to make sure that the tree
+# is not overfitting the training data, which can be done using cross-validation.
+
+# %%
+# Decision Tree Regression with Multi-Output Targets
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Here the :ref:`decision trees <tree>`
+# is used to predict simultaneously the noisy `x` and `y` observations of a circle
+# given a single underlying feature. As a result, it learns local linear
+# regressions approximating the circle.
+#
+# We can see that if the maximum depth of the tree (controlled by the
+# `max_depth` parameter) is set too high, the decision trees learn too fine
+# details of the training data and learn from the noise, i.e. they overfit.
+
+# %%
+# Create a random dataset
+# -----------------------
+rng = np.random.RandomState(1)
+X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
+y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
+y[::5, :] += 0.5 - rng.rand(20, 2)
+
+# %%
+# Fit regression model
+# --------------------
+regr_1 = DecisionTreeRegressor(max_depth=2)
+regr_2 = DecisionTreeRegressor(max_depth=5)
+regr_3 = DecisionTreeRegressor(max_depth=8)
+regr_1.fit(X, y)
+regr_2.fit(X, y)
+regr_3.fit(X, y)
+
+# %%
+# Predict
+# -------
+# Get predictions on the test set
+X_test = np.arange(-100.0, 100.0, 0.01)[:, np.newaxis]
+y_1 = regr_1.predict(X_test)
+y_2 = regr_2.predict(X_test)
+y_3 = regr_3.predict(X_test)
+
+# %%
+# Plot the results
+# ----------------
+plt.figure()
+s = 25
+plt.scatter(y[:, 0], y[:, 1], c="yellow", s=s, edgecolor="black", label="data")
+plt.scatter(
+    y_1[:, 0],
+    y_1[:, 1],
+    c="cornflowerblue",
+    s=s,
+    edgecolor="black",
+    label="max_depth=2",
+)
+plt.scatter(y_2[:, 0], y_2[:, 1], c="red", s=s, edgecolor="black", label="max_depth=5")
+plt.scatter(y_3[:, 0], y_3[:, 1], c="blue", s=s, edgecolor="black", label="max_depth=8")
+plt.xlim([-6, 6])
+plt.ylim([-6, 6])
+plt.xlabel("target 1")
+plt.ylabel("target 2")
+plt.title("Multi-output Decision Tree Regression")
+plt.legend(loc="best")
+plt.show()
+
+# %%
+# As you can see, the higher the value of `max_depth`, the more details of the data
+# are caught by the model. However, the model also overfits to the data and is
+# influenced by the noise.