Data Archive

Hyperparameter

Hyperparameters are parameters set before training that control the learning process, such as the number of nodes in a Neural network or k in KNN.

Techniques like GridSearchCV are used to explore the hyperparameter space and identify the best settings for model performance.

Hyperparameter tuning:

Hyperparameter Tuning

  1. Optimization Techniques:

    • Grid Search: Exhaustively searches through a specified subset of hyperparameters.
    • Random Search: Randomly samples from the hyperparameter space, often more efficient than grid search.
    • Advanced Methods: Bayesian optimization or evolutionary algorithms for more sophisticated searches.

  2. Objective:

    • Tune the model’s hyperparameters to improve performance. For example, in regularized linear regression, the main hyperparameter to tune is the regularization strength (e.g., alpha in Ridge or Lasso).
    • Use Cross Validation to evaluate the model’s performance with different hyperparameters.

Example of Hyperparameter Tuning with GridSearchCV

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
 
# Define the model
rf_regressor = RandomForestRegressor(random_state=42)
 
# Define the parameter grid
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10]
}
 
# Set up the grid search
grid_search = GridSearchCV(estimator=rf_regressor, param_grid=param_grid, cv=5, scoring='neg_mean_absolute_error')
 
# Fit the grid search
grid_search.fit(X_train, y_train)
 
# Get the best parameters
best_params = grid_search.best_params_
 
# Train the model with the best hyperparameters
rf_regressor = RandomForestRegressor(best_params, random_state=42)
rf_regressor.fit(X_train, y_train)

Key Considerations

  • Balance Between Exploration and Exploitation: Ensure a good balance between exploring the hyperparameter space and exploiting known good configurations.
  • Computational Resources: Consider the computational cost of hyperparameter tuning, especially with large datasets or complex models.
  • Cross Validation: Use cross-validation to ensure that the hyperparameter tuning process is robust and not overfitting to a particular train-test split.
  • Regularisation: Often part of the hyperparameter tuning process, especially in models prone to overfitting.

Graph View