Sklearn中GridSearchCV搭配MLPRegressor时random_state的正确配置位置及参数顺序影响最优结果的疑问

Let's break down your questions and address the unexpected behavior you're seeing:

1. Where to correctly place random_state when using GridSearchCV with MLPRegressor? #

The placement depends on your specific goal:

• For consistent, reproducible results across all parameter combinations: Place random_state directly in the MLPRegressor instantiation. This ensures every model trained during grid search uses the same seed for weight initialization, early stopping validation splits, and any other random processes in the MLP. Additionally, you can set random_state in GridSearchCV itself to fix cross-validation fold splits—this is critical for fair, reproducible comparison of different parameter combinations.
• For testing random seed variations per parameter combination: Place random_state in the param_grid (e.g., 'random_state': [0, 42, 123]) if you want to evaluate how random initialization affects specific model configurations. This is rarely needed unless you're explicitly studying model robustness to randomness.

2. Should random_state be set in MLPRegressor instantiation instead of param_grid? #

Yes, this is the recommended approach for most standard tuning scenarios (like yours, where you want stable, comparable results across parameter searches).

Here’s why your current setup caused inconsistent optimal results:
When you put random_state: [0] in param_grid, you’re telling GridSearchCV to set the seed for every MLP instance it creates—but the parallel execution (n_jobs=-1) can introduce subtle timing-based variability. More importantly, you weren’t fixing the cross-validation fold splits (unless you set random_state in GridSearchCV itself), which means the data splits used to evaluate each parameter combination might vary slightly when you reorder the hidden_layer_sizes list.

Even if you kept random_state fixed in param_grid, expanding your parameter space later could accidentally mix seed variations with your target tuning parameters, muddling the comparison of hidden layer structures or learning rates.

Fixed Code Example #

Here’s how to adjust your code for fully reproducible results:

# Set random_state directly in MLPRegressor to fix model initialization
MLP = MLPRegressor(random_state=0)
parameter_space = {
    'hidden_layer_sizes': [(32,), (32,16), (32,16,8), (32,16,8,4), (32,16,8,4,2), (32,32), (32,32,32), (32,32,32,32), (32,32,32,32,32), (16,8,4,2)],
    'activation': ['relu'],
    'solver': ['adam'],
    'learning_rate_init': [1, 0.1, 0.01, 0.001,0.0001,0.00001],
    'max_iter': [5000],
    'shuffle': [True, False],
    'early_stopping': [True, False],
    'n_iter_no_change': [50],
    # Remove random_state from param_grid
}
# Add random_state to GridSearchCV to fix cross-validation fold splits
gs_MLP = GridSearchCV(estimator=MLP, param_grid=parameter_space, cv=7, n_jobs=-1, random_state=0)
gs_MLP_fit = gs_MLP.fit(X, y)
print(gs_MLP.score(X,y))
print("Best parameters:", gs_MLP_fit.best_params_)

With this setup, both MLP initialization and cross-validation splits are fully fixed. Changing the order of hidden_layer_sizes won’t alter the optimal result—you’ll get consistent, reproducible grid search outcomes every time you run the code.

内容的提问来源于stack exchange，提问作者strateeg32