Scikit-Learn GridSearchCV PassiveAggressiveRegressor

Hyperparameter tuning is a crucial step in optimizing machine learning models for best performance. In this example, we’ll demonstrate how to use scikit-learn’s GridSearchCV to perform hyperparameter tuning for PassiveAggressiveRegressor, a model particularly effective for online learning and large-scale problems.

Grid search is a method for evaluating different combinations of model hyperparameters to find the best performing configuration. It exhaustively searches through a specified parameter grid, trains and evaluates the model for each combination using cross-validation, and selects the hyperparameters that yield the best performance metric.

PassiveAggressiveRegressor is used for large-scale learning with linear models, particularly effective for online learning. It adjusts the model with each new instance, aiming to make as few changes as possible to the existing model.

The key hyperparameters for PassiveAggressiveRegressor include the regularization strength (C), which controls the trade-off between fitting the data well and keeping the model coefficients small; the maximum number of iterations (max_iter) for the solver to converge; and the tube size (epsilon), within which no penalty is associated in the training loss with points predicted within a distance smaller than epsilon from the actual value.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import PassiveAggressiveRegressor

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=20, 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)

# Define parameter grid
param_grid = {
    'C': [0.01, 0.1, 1.0, 10.0],
    'max_iter': [100, 500, 1000],
    'epsilon': [0.01, 0.1, 0.2]
}

# Perform grid search
grid_search = GridSearchCV(estimator=PassiveAggressiveRegressor(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_squared_error')
grid_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {grid_search.best_score_:.3f}")
print(f"Best parameters: {grid_search.best_params_}")

# Evaluate on test set
best_model = grid_search.best_estimator_
test_score = best_model.score(X_test, y_test)
print(f"Test set score: {test_score:.3f}")

Running the example gives an output like:

Best score: -0.012
Best parameters: {'C': 0.1, 'epsilon': 0.2, 'max_iter': 100}
Test set score: 1.000

The steps are as follows:

1. Generate a synthetic regression dataset using make_regression.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for C, max_iter, and epsilon.
4. Perform grid search using GridSearchCV, specifying the PassiveAggressiveRegressor model, parameter grid, 5-fold cross-validation, and mean squared error scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by grid search.
6. Evaluate the best model on the hold-out test set and report the score.

By using GridSearchCV, we can easily explore different hyperparameter settings and find the combination that maximizes the model’s performance. This automated approach saves time and effort compared to manual hyperparameter tuning and helps ensure we select the best configuration for our PassiveAggressiveRegressor model.