Task 1: Assign different combination of max_dept and min_samples_leaf and min_samples_split to param_grid - 'max_depth': [28, 30, 32, 34, 36], 'min_samples_leaf': [5, 10, 15, 12],'min_samples_split': [120, 128, 136]
Task 2: Calculate the best parameter using GridSearchCV and store it in grid_search. Print the parameters.
Hint
from sklearn.model_selection import GridSearchCV
param_grid = [
{'max_depth': [28, 30, 32, 34, 36], 'min_samples_leaf': [5, 10, 15, 12],'min_samples_split': [120, 128, 136]},
]
grid_search = GridSearchCV(rfc_clf, param_grid, cv=5, scoring='neg_mean_squared_error')
grid_search.fit(train_set[['xformHr', 'temp','weekday']], train_set['cntDeTrended'])
print(grid_search.best_params_)
feature_importances = grid_search.best_estimator_.feature_importances_
Task 3: Fit the training dataset to the calculated best parameter model using the fit() method.
Task 4: Complete the code to calculate the importance score for each of the feature.
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