Configure GradientBoostingRegressor "random_state" Parameter

The random_state parameter in scikit-learn’s GradientBoostingRegressor controls the random number generator used for initialization and shuffling the data.

Gradient Boosting Regressor is a powerful ensemble learning method that builds a model in a stage-wise fashion from multiple weak learners (usually decision trees) to minimize the loss function.

The random_state parameter ensures the reproducibility of the results by setting a seed for the random number generation process.

The default value for random_state is None, which means the random number generator is the RandomState instance used by np.random.

In practice, setting random_state to an integer value, such as 0 or 42, is common to make the results reproducible.

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=1000, n_features=10, noise=0.1, random_state=42)

# 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=42)

# Train with different random_state values
random_state_values = [None, 0, 42]
mse_values = []

for state in random_state_values:
    gbr = GradientBoostingRegressor(random_state=state)
    gbr.fit(X_train, y_train)
    y_pred = gbr.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    mse_values.append(mse)
    print(f"random_state={state}, Mean Squared Error: {mse:.3f}")

Running the example gives an output like:

random_state=None, Mean Squared Error: 1234.942
random_state=0, Mean Squared Error: 1245.666
random_state=42, Mean Squared Error: 1234.753

The key steps in this example are:

Generate a synthetic regression dataset with make_regression.
Split the data into train and test sets.
Train GradientBoostingRegressor models with different random_state values.
Evaluate the mean squared error of each model on the test set.

Some tips and heuristics for setting random_state:

Use an integer value for random_state to ensure reproducibility in experiments.
Common values are 0, 42, or any other integer to keep results consistent across runs.
If reproducibility is not a concern, leaving random_state as None is acceptable.

Issues to consider:

Different random_state values can lead to slightly different results due to the nature of random initialization and data shuffling.
Ensuring reproducibility is crucial in research and when comparing model performances.

See Also