Scikit-Learn RandomizedSearchCV LinearSVR

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 LinearSVR 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.

LinearSVR is a linear support vector regression model that fits a linear model to the data by minimizing the hinge loss function, designed for regression problems.

Key hyperparameters for LinearSVR include the regularization parameter (C), which controls the trade-off between achieving a low training error and a low testing error; epsilon, which defines the epsilon-tube within which no penalty is associated with errors; loss, which specifies the loss function to be used; and dual, a boolean parameter that determines whether to solve the dual or primal optimization problem.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.svm import LinearSVR
from scipy.stats import uniform

# 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 = LinearSVR(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'C': uniform(loc=0.01, scale=10),
    'epsilon': uniform(loc=0, scale=1),
    'loss': ['epsilon_insensitive', 'squared_epsilon_insensitive'],
    'dual': [True, False]
}

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

Running the example gives an output like:

Best score: -0.010
Best parameters: {'C': 9.142405525564714, 'dual': False, 'epsilon': 0.005184862773986776, 'loss': 'squared_epsilon_insensitive'}
Test set MSE: -1.000

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the LinearSVR model.
4. Define the hyperparameter distribution with different values for C, epsilon, loss, and dual hyperparameters.
5. Perform random search using RandomizedSearchCV, specifying the LinearSVR model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and negative mean squared error as the scoring metric.
6. Report the best cross-validation score and the best set of hyperparameters found by random search.
7. Evaluate the best model on the hold-out 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 LinearSVR model.