Configure MLPRegressor "random_state" Parameter

The random_state parameter in scikit-learn’s MLPRegressor controls the randomness of weight and bias initialization, as well as the shuffling of training data.

Multi-layer Perceptron (MLP) is a type of neural network that can be used for regression tasks. The random_state parameter ensures reproducibility of results by fixing the random number generator seed.

Setting random_state to a specific integer value ensures that the same random numbers are generated each time the model is initialized, leading to consistent results across multiple runs.

The default value for random_state is None, which means that a different random seed is used each time the model is run.

In practice, random_state is often set to a fixed integer (e.g., 42) for reproducibility, or to multiple different values to assess the model’s stability.

from sklearn.neural_network import MLPRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np

# 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, 123, 456]
mse_scores = []

for rs in random_state_values:
    mlp = MLPRegressor(hidden_layer_sizes=(100,), max_iter=500, random_state=rs)
    mlp.fit(X_train, y_train)
    y_pred = mlp.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    mse_scores.append(mse)
    print(f"random_state={rs}, MSE: {mse:.3f}")

# Calculate variance of MSE scores
mse_variance = np.var(mse_scores)
print(f"Variance of MSE scores: {mse_variance:.6f}")

Running the example gives an output like:

random_state=None, MSE: 128.429
random_state=42, MSE: 139.311
random_state=123, MSE: 118.676
random_state=456, MSE: 132.617
Variance of MSE scores: 55.999839

The key steps in this example are:

Generate a synthetic regression dataset
Split the data into train and test sets
Train MLPRegressor models with different random_state values
Evaluate the mean squared error (MSE) of each model on the test set
Calculate the variance of MSE scores to assess model stability

Some tips for setting random_state:

Use a fixed value (e.g., 42) for reproducibility in experiments and debugging
Set to None or use different values to assess model stability
When comparing models, use the same random_state to ensure fair comparison

Issues to consider:

Different random_state values can lead to different model performance
A low variance in performance across different seeds suggests a stable model
In production, consider using ensemble methods to reduce dependency on a single random initialization

See Also