gradient-boosting-regression.py

#https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
#         Maria Telenczuk <https://github.com/maikia>
#         Katrina Ni <https://github.com/nilichen>
#
# License: BSD 3 clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn import datasets, ensemble
from sklearn.inspection import permutation_importance
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.1, random_state=13
)

params = {
    "n_estimators": 500,
    "max_depth": 4,
    "min_samples_split": 5,
    "learning_rate": 0.01,
    "loss": "squared_error",
}

#Fit regression model 
reg = ensemble.GradientBoostingRegressor(**params)
reg.fit(X_train, y_train)

mse = mean_squared_error(y_test, reg.predict(X_test))
print("The mean squared error (MSE) on test set: {:.4f}".format(mse))

# Plot training deviance
test_score = np.zeros((params["n_estimators"],), dtype=np.float64)
for i, y_pred in enumerate(reg.staged_predict(X_test)):
    test_score[i] = mean_squared_error(y_test, y_pred)

fig = plt.figure(figsize=(6, 6))
plt.subplot(1, 1, 1)
plt.title("Deviance")
plt.plot(
    np.arange(params["n_estimators"]) + 1,
    reg.train_score_,
    "b-",
    label="Training Set Deviance",
)
plt.plot(
    np.arange(params["n_estimators"]) + 1, test_score, "r-", label="Test Set Deviance"
)
plt.legend(loc="upper right")
plt.xlabel("Boosting Iterations")
plt.ylabel("Deviance")
fig.tight_layout()
plt.show()

# Plot feature importance¶
feature_importance = reg.feature_importances_
sorted_idx = np.argsort(feature_importance)
pos = np.arange(sorted_idx.shape[0]) + 0.5
fig = plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.barh(pos, feature_importance[sorted_idx], align="center")
plt.yticks(pos, np.array(diabetes.feature_names)[sorted_idx])
plt.title("Feature Importance (MDI)")

result = permutation_importance(
    reg, X_test, y_test, n_repeats=10, random_state=42, n_jobs=2
)
sorted_idx = result.importances_mean.argsort()
plt.subplot(1, 2, 2)
plt.boxplot(
    result.importances[sorted_idx].T,
    vert=False,
    labels=np.array(diabetes.feature_names)[sorted_idx],
)
plt.title("Permutation Importance (test set)")
fig.tight_layout()
plt.show()