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Decision Tree Regression#

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

Decision Tree on a 1D Regression Task#

Here we fit a tree on a 1D regression task.

The decision trees 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#

importnumpyasnp
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

fromsklearn.treeimport DecisionTreeRegressor
regr_1 = DecisionTreeRegressor (max_depth=2)
regr_2 = DecisionTreeRegressor (max_depth=5)
regr_1.fit(X, y)
regr_2.fit(X, y)

DecisionTreeRegressor(max_depth=5)

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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#

importmatplotlib.pyplotasplt
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)
plt.plot (X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
plt.xlabel ("data")
plt.ylabel ("target")
plt.title ("Decision Tree Regression")
plt.legend ()
plt.show ()

Decision Tree Regression

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.