bagging decision tree regression code example

Example 1: sklearn decision tree regressor

Import the necessary modules and libraries
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 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()

Example 2: Decision tree learning algorithm for regression

# Decision tree learning algorithm for regression

from pyspark.ml.linalg import Vectors
df = spark.createDataFrame([
  (1.0, Vectors.dense(1.0)),
  (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
dt = DecisionTreeRegressor(maxDepth=2, varianceCol="variance")
model = dt.fit(df)
model.depth
# 1
model.numNodes
# 3
model.featureImportances
# SparseVector(1, {0: 1.0}
model.numFeatures
# 1
test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
model.transform(test0).head().prediction
# 0.0
test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
model.transform(test1).head().prediction
# 1.0
dtr_path = temp_path + "/dtr"
dt.save(dtr_path)
dt2 = DecisionTreeRegressor.load(dtr_path)
dt2.getMaxDepth()
# 2
model_path = temp_path + "/dtr_model"
model.save(model_path)
model2 = DecisionTreeRegressionModel.load(model_path)
model.numNodes == model2.numNodes
# True
model.depth == model2.depth
# True
model.transform(test1).head().variance
# 0.0