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%matplotlib inline


This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems. Here, we will train a model to tackle a diabetes regression task. We will obtain the results from :class:~sklearn.ensemble.GradientBoostingRegressor with least squares loss and 500 regression trees of depth 4.

Note: For larger datasets (n_samples >= 10000), please refer to :class:~sklearn.ensemble.HistGradientBoostingRegressor.

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print(__doc__)

# Author: Peter Prettenhofer <[email protected]>
#         Maria Telenczuk <https://github.com/maikia>
#         Katrina Ni <https://github.com/nilichen>
#

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


First we need to load the data.

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


## Data preprocessing¶

Next, we will split our dataset to use 90% for training and leave the rest for testing. We will also set the regression model parameters. You can play with these parameters to see how the results change.

n_estimators : the number of boosting stages that will be performed. Later, we will plot deviance against boosting iterations.

max_depth : limits the number of nodes in the tree. The best value depends on the interaction of the input variables.

min_samples_split : the minimum number of samples required to split an internal node.

learning_rate : how much the contribution of each tree will shrink.

loss : loss function to optimize. The least squares function is used in this case however, there are many other options (see :class:~sklearn.ensemble.GradientBoostingRegressor ).

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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': 'ls'}


## Fit regression model¶

Now we will initiate the gradient boosting regressors and fit it with our training data. Let's also look and the mean squared error on the test data.

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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¶

Finally, we will visualize the results. To do that we will first compute the test set deviance and then plot it against boosting iterations.

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test_score = np.zeros((params['n_estimators'],), dtype=np.float64)
for i, y_pred in enumerate(reg.staged_predict(X_test)):
test_score[i] = reg.loss_(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¶

Careful, impurity-based feature importances can be misleading for high cardinality features (many unique values). As an alternative, the permutation importances of reg can be computed on a held out test set. See permutation_importance for more details.

For this example, the impurity-based and permutation methods identify the same 2 strongly predictive features but not in the same order. The third most predictive feature, "bp", is also the same for the 2 methods. The remaining features are less predictive and the error bars of the permutation plot show that they overlap with 0.

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feature_importance = reg.feature_importances_
sorted_idx = np.argsort(feature_importance)
pos = np.arange(sorted_idx.shape[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()