Hyperparameter tuning using Grid Search

GridSearchCV.py

#import libraries
from sklearn import datasets
from catboost import CatBoostClassifier
from sklearn.model_selection import GridSearchCV
# load data
cancer = datasets.load_breast_cancer()

# target
y = cancer.target

# features
X = cancer.data

#Instantiate CatBoostClassifier
cbc = CatBoostClassifier()

#create the grid
grid = {'max_depth': [3,4,5],'n_estimators':[100, 200, 300]}

#Instantiate GridSearchCV
gscv = GridSearchCV (estimator = cbc, param_grid = grid, scoring ='accuracy', cv = 5)

#fit the model
gscv.fit(X,y)

#returns the estimator with the best performance
print(gscv.best_estimator_)

#returns the best score
print(gscv.best_score_)

#returns the best parameters
print(gscv.best_params_)