Tune hyperparameters using Grid search and Randomised search Cross Validation

hyperparameters_tuning.py

# Tune hyperparameters using Grid search and Randomised search Cross Validation

from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV

# Grid Search Cross Validation
# Specify discrete values for hyperparameters
param_grid = [
    {'max_depth': [1, 20, 100], 'max_features': [1, 5, 15, 20],
     'max_leaf_nodes': [5, 50, 100]},
]
search = GridSearchCV(estimator=model, param_grid=param_grid, cv=10,
                      scoring='neg_mean_squared_error', return_train_score=True)

# Randomised search Cross Validation
# Specify distribution for hyperparameters
param_distribs = {
  'max_depth': randint(low=1, high=10),
  'max_features': randint(low=1, high=20),
}
search = RandomizedSearchCV(estimator=model, param_distributions=param_distributions,
                            n_iter=5, cv=10, scoring='neg_mean_squared_error',
                            random_state=42, return_train_score=True)