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"""
This module sets up a K-Nearest Neighbors Regressor with hyperparameter tuning.
Features:
- Uses `KNeighborsRegressor` estimator from scikit-learn.
- Defines a hyperparameter grid for neighbor parameters.
- Non-parametric method useful for capturing local patterns.
Special Considerations:
- Feature scaling is crucial for KNN.
- Sensitive to the choice of `n_neighbors`.
- Training is fast, but prediction can be slow on large datasets.
"""
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# Define the estimator
estimator = KNeighborsRegressor(n_jobs=-1)
# Define the hyperparameter grid
param_grid = {
'model__n_neighbors': [3, 5, 7], # Focus on common neighbor values
'model__weights': ['uniform', 'distance'], # Standard options
'model__algorithm': ['auto', 'ball_tree'], # Reduce algorithms to commonly used ones
'model__p': [1, 2], # Manhattan and Euclidean distances
'preprocessor__num__imputer__strategy': ['mean'], # Single imputation strategy
'preprocessor__num__scaler__with_mean': [True], # StandardScaler
'preprocessor__num__scaler__with_std': [True], # StandardScaler
}
# Optional: Define the default scoring metric
default_scoring = 'neg_root_mean_squared_error'
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