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"""
This module sets up a Multilayer Perceptron Regressor with hyperparameter tuning.
Features:
- Uses `MLPRegressor` estimator from scikit-learn.
- Defines a hyperparameter grid for neural network parameters.
- Capable of capturing complex non-linear relationships.
Special Considerations:
- Feature scaling is crucial for MLP.
- May produce convergence warnings; increase `max_iter` to address this.
- Can be sensitive to hyperparameter settings; tuning is important.
"""
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# Define the estimator
estimator = MLPRegressor(random_state=42, max_iter=1000)
# Define the hyperparameter grid
param_grid = {
'model__hidden_layer_sizes': [(50,), (100,), (50, 50)], # Simplified layer sizes
'model__activation': ['relu'], # Focused on ReLU, the most commonly effective activation
'model__solver': ['adam'], # Retain 'adam' for efficiency; drop 'lbfgs' (slower for larger datasets)
'model__alpha': [0.0001, 0.001], # Regularization strengths
'model__learning_rate': ['constant', 'adaptive'], # Common learning rate strategies
'preprocessor__num__imputer__strategy': ['mean'], # Single imputation strategy
'preprocessor__num__scaler__with_mean': [True], # StandardScaler
'preprocessor__num__scaler__with_std': [True], # StandardScaler
}
# Optional: Default scoring metric
default_scoring = 'neg_root_mean_squared_error'
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