Decision Tree.py

import numpy as np
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
# 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 result
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( )