Scikit-Learn RandomizedSearchCV MultinomialNB

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 MultinomialNB model, commonly used for multiclass 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.

MultinomialNB is a Naive Bayes classifier suited for classification with discrete features. It estimates the probability of a class given a feature vector and assumes feature independence.

Key hyperparameters for MultinomialNB include the smoothing parameter (alpha), which prevents zero probabilities for unseen features, and fit_prior, which determines whether to learn class prior probabilities or use uniform priors.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.naive_bayes import MultinomialNB
from scipy.stats import uniform
import numpy as np

# Generate synthetic multiclass classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_classes=3, random_state=42)
X = np.abs(X)

# 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 = MultinomialNB()

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0, scale=1),
    'fit_prior': [True, False]
}

# 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: 0.426
Best parameters: {'alpha': 0.05808361216819946, 'fit_prior': False}
Test set accuracy: 0.450

The steps are as follows:

1. Generate a synthetic multiclass classification dataset using make_classification.
2. Split the dataset into train and test sets using train_test_split.
3. Define the MultinomialNB model.
4. Specify the hyperparameter distribution: alpha sampled from a uniform distribution and fit_prior as [True, False].
5. Perform random search using RandomizedSearchCV, specifying the MultinomialNB model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring.
6. Report the best cross-validation score and best set of hyperparameters.
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 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 MultinomialNB model.