Scikit-Learn RandomizedSearchCV PLSRegression

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 PLSRegression 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.

Partial Least Squares Regression (PLSRegression) is used for regression tasks involving many highly collinear variables. It models relationships between input features and response variables by extracting latent variables.

The most important hyperparameter for PLSRegression is the number of components (n_components), which determines the number of latent variables to extract.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.cross_decomposition import PLSRegression
from sklearn.metrics import mean_squared_error
from scipy.stats import randint

# 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 = PLSRegression()

# Define hyperparameter distribution
param_dist = {
    'n_components': randint(1, 21)
}

# 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 = mean_squared_error(y_test, best_model.predict(X_test))
print(f"Test set MSE: {mse:.3f}")

Running the example gives an output like:

Best score: 0.010
Best parameters: {'n_components': 5}
Test set MSE: 0.010

The steps are as follows:

1. Generate a synthetic regression dataset using make_regression.
2. Split the dataset into training and test sets using train_test_split.
3. Define the PLSRegression model.
4. Define the hyperparameter distribution for n_components, ranging from 1 to 20.
5. Use RandomizedSearchCV to perform random search with 100 iterations, 5-fold cross-validation, and mean squared error scoring.
6. Output the best cross-validation score and the best set of hyperparameters.
7. Evaluate the best model on the 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 PLSRegression model.