Scikit-Learn RandomizedSearchCV Perceptron

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 Perceptron 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.

Perceptron is a linear classifier that updates weights based on misclassified examples. It is often used for binary classification tasks where the goal is to separate two classes with a linear decision boundary.

Key hyperparameters for Perceptron include the learning rate (eta0), which controls the step size in the weight update rule; the maximum number of iterations (max_iter), which defines how many times the training data is iterated over; and the penalty type (penalty), which determines the regularization applied to prevent overfitting.

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

# 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 the model
model = Perceptron(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'eta0': uniform(0.0001, 0.1),
    'max_iter': randint(50, 500),
    'penalty': ['l2', 'l1', 'elasticnet']
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   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: 0.776
Best parameters: {'eta0': 0.07732447692966575, 'max_iter': 185, 'penalty': 'elasticnet'}
Test set accuracy: 0.795

The steps are as follows:

1. Generate a synthetic binary classification dataset using make_classification.
2. Split the dataset into train and test sets using train_test_split.
3. Define the Perceptron model.
4. Define the hyperparameter distribution with different values for eta0, max_iter, and penalty.
5. Perform random search using RandomizedSearchCV, specifying the Perceptron model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
6. Report the best cross-validation score and best set of hyperparameters found by random search.
7. 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 Perceptron 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 Perceptron model.