Configure RandomForestRegressor "random_state" Parameter

The random_state parameter in scikit-learn’s RandomForestRegressor controls the randomness of the model training process.

Random Forest is an ensemble learning method that combines predictions from multiple decision trees. The random_state parameter sets the seed for the random number generator used in the model’s training.

Setting random_state to a fixed value ensures that the same sequence of random numbers is generated each time the model is trained, leading to reproducible results. If random_state is set to None (default), a different sequence of random numbers is used each time, resulting in slightly different models.

The default value for random_state is None. In practice, values like 42, 0, or any other integer are commonly used to ensure reproducibility.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
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)

# Train with different random_state values
random_state_values = [None, 42, 0, 100]
mse_scores = []

for rs in random_state_values:
    rf = RandomForestRegressor(n_estimators=100, random_state=rs)
    rf.fit(X_train, y_train)
    y_pred = rf.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    mse_scores.append(mse)
    print(f"random_state={rs}, MSE: {mse:.3f}")

Running the example gives an output like:

random_state=None, MSE: 2573.937
random_state=42, MSE: 2621.793
random_state=0, MSE: 2744.808
random_state=100, MSE: 2594.465

The key steps in this example are:

Generate a synthetic regression dataset with noise
Split the data into train and test sets
Train RandomForestRegressor models with different random_state values
Evaluate the mean squared error of each model on the test set

Tips and heuristics for setting random_state:

Use a fixed value for reproducibility and consistency in model evaluation and comparison
Set random_state to None if you want different results each time the model is trained

Issues to consider:

Reproducibility is crucial in production environments and for sharing research results
Using different random_state values can lead to slightly different model performances

See Also