Configure VotingRegressor "n_jobs" Parameter

The n_jobs parameter in scikit-learn’s VotingRegressor controls the number of CPU cores used for parallel processing during fitting and prediction.

VotingRegressor is an ensemble method that combines multiple base regressors to make predictions based on a voting strategy. It can leverage parallel processing to speed up computations when working with multiple estimators or large datasets.

The n_jobs parameter determines how many CPU cores are used for parallel execution. A value of -1 uses all available cores, 1 means no parallel processing (sequential execution), and positive integers specify the exact number of cores to use.

By default, n_jobs is set to None, which is equivalent to 1 (no parallel processing).

Common values for n_jobs include -1 (all cores), 1 (no parallelism), or a positive integer up to the number of available CPU cores on the machine.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import time

# Generate synthetic dataset
X, y = make_regression(n_samples=10000, n_features=20, 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 base models
base_models = [
    ('lr', LinearRegression()),
    ('svr', SVR()),
    ('rf', RandomForestRegressor(n_estimators=100, random_state=42))
]

# Train with different n_jobs values
n_jobs_values = [-1, 1, 2, 4]
results = []

for n_jobs in n_jobs_values:
    voter = VotingRegressor(estimators=base_models, n_jobs=n_jobs)

    start_time = time.time()
    voter.fit(X_train, y_train)
    fit_time = time.time() - start_time

    start_time = time.time()
    y_pred = voter.predict(X_test)
    predict_time = time.time() - start_time

    mse = mean_squared_error(y_test, y_pred)
    results.append((n_jobs, fit_time, predict_time, mse))

    print(f"n_jobs={n_jobs}, Fit time: {fit_time:.2f}s, Predict time: {predict_time:.2f}s, MSE: {mse:.4f}")

Running the example gives an output like:

n_jobs=-1, Fit time: 13.69s, Predict time: 1.42s, MSE: 2985.9528
n_jobs=1, Fit time: 14.10s, Predict time: 1.30s, MSE: 2985.9528
n_jobs=2, Fit time: 13.48s, Predict time: 1.45s, MSE: 2985.9528
n_jobs=4, Fit time: 13.88s, Predict time: 1.31s, MSE: 2985.9528

The key steps in this example are:

Generate a synthetic regression dataset
Split the data into train and test sets
Define base models for the VotingRegressor
Train VotingRegressor models with different n_jobs values
Measure fit time, prediction time, and mean squared error for each configuration

Some tips and heuristics for setting n_jobs:

Use -1 to utilize all available CPU cores for maximum parallelism
For small datasets or simple models, parallelism may not provide significant speedup
Consider the memory usage when using multiple cores, as it may increase with parallelism

Issues to consider:

The optimal n_jobs value depends on the hardware, dataset size, and complexity of base models
Excessive parallelism can lead to overhead and diminishing returns in performance gains
Some operations may not benefit from parallelism, so always benchmark to confirm improvements

See Also