Scikit-Learn GridSearchCV Perceptron

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 Perceptron, a simple linear algorithm for binary classification tasks.

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.

Perceptron is a linear model for binary classification that updates its weights based on misclassified examples during training. The model iteratively adjusts its parameters to minimize classification errors.

The key hyperparameters for Perceptron include the regularization term (penalty), which helps avoid overfitting; the regularization strength (alpha), which controls the degree of regularization applied; and the maximum number of iterations (max_iter) over the training data.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import Perceptron

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, 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 parameter grid
param_grid = {
    'penalty': [None, 'l2', 'l1', 'elasticnet'],
    'alpha': [0.0001, 0.001, 0.01],
    'max_iter': [1000, 2000, 3000]
}

# Perform grid search
grid_search = GridSearchCV(estimator=Perceptron(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='accuracy')
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_
accuracy = best_model.score(X_test, y_test)
print(f"Test set accuracy: {accuracy:.3f}")

Running the example gives an output like:

Best score: 0.747
Best parameters: {'alpha': 0.0001, 'max_iter': 1000, 'penalty': 'l2'}
Test set accuracy: 0.780

The steps are as follows:

1. Generate a synthetic binary classification dataset using make_classification.
2. Split the dataset into training and testing sets using train_test_split.
3. Define the parameter grid with different values for penalty, alpha, and max_iter hyperparameters.
4. Perform grid search using GridSearchCV, specifying the Perceptron model, parameter grid, 5-fold cross-validation, and accuracy scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by the grid search.
6. Evaluate the best model on the hold-out test set and report the accuracy.

By using GridSearchCV, we can efficiently explore different hyperparameter settings to find the optimal configuration for the Perceptron model, ensuring the best performance for binary classification tasks.