Configure VotingRegressor "verbose" Parameter

The verbose parameter in scikit-learn’s VotingRegressor controls the level of output during fitting and prediction.

VotingRegressor is an ensemble method that combines predictions from multiple base regressors to produce a final prediction. The verbose parameter determines how much information is displayed during the model’s operations.

Higher verbose values provide more detailed output, which can be useful for debugging or monitoring progress, especially with large datasets or complex models.

The default value for verbose is 0, which means no output is produced. Common values are 0 (no output), 1 (some output), and 2 (detailed output).

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.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error

# Generate synthetic 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 base models
base_models = [
    ('lr', LinearRegression()),
    ('rf', RandomForestRegressor(n_estimators=10, random_state=42)),
    ('svr', SVR(kernel='rbf'))
]

# Train with different verbose values
verbose_values = [0, 1, 2]

for verbose in verbose_values:
    vr = VotingRegressor(estimators=base_models, verbose=verbose)
    vr.fit(X_train, y_train)
    y_pred = vr.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"verbose={verbose}, MSE: {mse:.3f}")

Running the example gives an output like:

verbose=0, MSE: 2652.396
[Voting] ....................... (1 of 3) Processing lr, total=   0.0s
[Voting] ....................... (2 of 3) Processing rf, total=   0.1s
[Voting] ...................... (3 of 3) Processing svr, total=   0.0s
verbose=1, MSE: 2652.396
[Voting] ....................... (1 of 3) Processing lr, total=   0.0s
[Voting] ....................... (2 of 3) Processing rf, total=   0.1s
[Voting] ...................... (3 of 3) Processing svr, total=   0.0s
verbose=2, MSE: 2652.396

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 verbose values
Evaluate the mean squared error of each model on the test set

Some tips for using the verbose parameter:

Use verbose=0 for production code or when running many experiments
Set verbose=1 or verbose=2 when debugging or monitoring long-running fits
Higher verbose values can slow down execution, so use judiciously

Issues to consider:

The amount of output can be overwhelming with large datasets or many estimators
Verbose output may not be necessary for simple models or small datasets
In some environments, excessive output might interfere with other logging or monitoring systems

See Also