50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
|
"""
|
||
|
===================================================================
|
||
|
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.
|
||
|
"""
|
||
|
|
||
|
# Import the necessary modules and libraries
|
||
|
import matplotlib.pyplot as plt
|
||
|
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
|
||
|
regr_1 = DecisionTreeRegressor(max_depth=2)
|
||
|
regr_2 = DecisionTreeRegressor(max_depth=5)
|
||
|
regr_1.fit(X, y)
|
||
|
regr_2.fit(X, y)
|
||
|
|
||
|
# Predict
|
||
|
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
|
||
|
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()
|