# Gradient Boosting regression#

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
`GradientBoostingRegressor`

with least squares loss
and 500 regression trees of depth 4.

Note: For larger datasets (n_samples >= 10000), please refer to
`HistGradientBoostingRegressor`

. See
Features in Histogram Gradient Boosting Trees for an example
showcasing some other advantages of
`HistGradientBoostingRegressor`

.

```
# 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
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
from sklearn.utils.fixes import parse_version
```

## Load the data#

First we need to load the data.

```
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
`GradientBoostingRegressor`

).

```
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#

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.

```
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))
```

```
The mean squared error (MSE) on test set: 3044.4733
```

## 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.

```
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#

Warning

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 feature 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.

```
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)
# `labels` argument in boxplot is deprecated in matplotlib 3.9 and has been
# renamed to `tick_labels`. The following code handles this, but as a
# scikit-learn user you probably can write simpler code by using `labels=...`
# (matplotlib < 3.9) or `tick_labels=...` (matplotlib >= 3.9).
tick_labels_parameter_name = (
"tick_labels"
if parse_version(matplotlib.__version__) >= parse_version("3.9")
else "labels"
)
tick_labels_dict = {
tick_labels_parameter_name: np.array(diabetes.feature_names)[sorted_idx]
}
plt.boxplot(result.importances[sorted_idx].T, vert=False, **tick_labels_dict)
plt.title("Permutation Importance (test set)")
fig.tight_layout()
plt.show()
```

**Total running time of the script:** (0 minutes 1.439 seconds)

Related examples

Gradient Boosting regularization

Feature importances with a forest of trees

Gradient Boosting Out-of-Bag estimates

Permutation Importance with Multicollinear or Correlated Features