Scikit-Learn RandomizedSearchCV PassiveAggressiveRegressor

Hyperparameter tuning is essential for optimizing machine learning models. In this example, we’ll demonstrate how to use scikit-learn’s RandomizedSearchCV for hyperparameter tuning of a PassiveAggressiveRegressor model, commonly used for regression tasks.

Random search is a method for evaluating different combinations of model hyperparameters. Unlike grid search, it samples a fixed number of hyperparameter combinations from a specified distribution, making it more efficient when searching over a large hyperparameter space.

PassiveAggressiveRegressor is a linear model used for regression tasks. It is particularly effective for large-scale learning and can handle high-dimensional data.

Key hyperparameters for PassiveAggressiveRegressor include the regularization parameter (C), which controls the model’s complexity and helps prevent overfitting; the max_iter, which determines the maximum number of passes over the training data; and the tol, which sets the tolerance for stopping criteria.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import PassiveAggressiveRegressor
from scipy.stats import uniform

# 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)

# Define the model
model = PassiveAggressiveRegressor(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'C': uniform(loc=0, scale=4),
    'max_iter': [1000, 2000, 3000],
    'tol': uniform(loc=1e-4, scale=1e-3)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   cv=5,
                                   scoring='neg_mean_squared_error',
                                   random_state=42)
random_search.fit(X_train, y_train)

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

# Evaluate on test set
best_model = random_search.best_estimator_
mse = -best_model.score(X_test, y_test)
print(f"Test set mean squared error: {mse:.3f}")

Running the example gives an output like:

Best score: 0.013
Best parameters: {'C': 0.053059844639466114, 'max_iter': 1000, 'tol': 0.0010656320330745593}
Test set mean squared error: -1.000

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the model then the hyperparameter distribution with different values for C, max_iter, and tol hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the PassiveAggressiveRegressor model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and mean squared error scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by random search.
6. Evaluate the best model on the hold-out test set and report the mean squared error.

By using RandomizedSearchCV, we can efficiently 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.