66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
|
"""
|
||
|
|
===================================================================
|
||
|
|
Multi-output Decision Tree Regression
|
||
|
|
===================================================================
|
||
|
|
|
||
|
|
An example to illustrate multi-output regression with decision tree.
|
||
|
|
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
|
||
|
|
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(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
|
||
|
|
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="navy", 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="orange", 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()
|