Scikit-Learn GradientBoostingRegressor Model

Gradient Boosting is a powerful ensemble technique for regression problems, combining multiple weak learners to create a strong predictive model. GradientBoostingRegressor in scikit-learn is used to predict continuous target variables by correcting errors of sequential models.

The key hyperparameters of GradientBoostingRegressor include n_estimators (the number of boosting stages), learning_rate (which shrinks the contribution of each tree), and max_depth (the maximum depth of the individual decision trees). This algorithm is suitable for regression problems.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error

# generate synthetic regression dataset
X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=1)

# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# create model
model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=1)

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
mse = mean_squared_error(y_test, yhat)
print('Mean Squared Error: %.3f' % mse)

# make a prediction
row = [[-0.806091, 0.625232, -0.603220, -0.211913, -0.823170]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 652.478
Predicted: -33.985

The steps are as follows:

1. Generate a synthetic regression dataset using the make_regression() function. This creates a dataset with a specified number of samples (n_samples), features (n_features), and a fixed random seed (random_state) for reproducibility. The dataset is split into training and test sets using train_test_split().

2. Instantiate a GradientBoostingRegressor model with 100 estimators, a learning rate of 0.1, and a maximum depth of 3. The model is then fit on the training data using the fit() method.

3. Evaluate the model’s performance by comparing the predictions (yhat) to the actual values (y_test) using the mean squared error metric.

4. Make a single prediction by passing a new data sample to the predict() method.

This example demonstrates how to set up and use a GradientBoostingRegressor model for regression tasks, highlighting the efficiency and accuracy of this algorithm in scikit-learn. The model can be fit directly on the training data and used to make predictions on new data, making it a practical choice for real-world regression problems.