Scikit-Learn RandomizedSearchCV LogisticRegression

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 logistic regression model, commonly used for binary classification 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.

Logistic regression is a linear model used for binary classification. It estimates the probability of a binary outcome and learns a decision boundary based on input features. The model is trained by minimizing the logistic loss function.

Key hyperparameters for logistic regression include the regularization strength (C), which controls model complexity and helps prevent overfitting; the penalty type (l1 or l2), which determines the type of regularization applied; and the solver algorithm, which is the optimization method used to find the model coefficients.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from scipy.stats import uniform

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, 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 hyperparameter distribution
param_dist = {
    'C': uniform(loc=0, scale=4),
    'penalty': ['l1', 'l2'],
    'solver': ['liblinear', 'saga']
}

# Perform random search
random_search = RandomizedSearchCV(estimator=LogisticRegression(random_state=42),
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   cv=5,
                                   scoring='accuracy',
                                   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_
accuracy = best_model.score(X_test, y_test)
print(f"Test set accuracy: {accuracy:.3f}")

Running the example gives an output like:

Best score: 0.830
Best parameters: {'C': 2.9279757672456204, 'penalty': 'l1', 'solver': 'liblinear'}
Test set accuracy: 0.790

The steps are as follows:

1. Generate a synthetic binary classification dataset using scikit-learn’s make_classification function.
2. Split the dataset into train and test sets using train_test_split.
3. Define a hyperparameter distribution with different values for C, penalty, and solver hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the LogisticRegression model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy 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 accuracy.

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 logistic regression model.