Hyperparameter tuning using RandomizedSearchCV

RandomizedSearchCV.py

#Import libraries
import numpy as np
from scipy.stats import randint
from catboost import CatBoostClassifier
from sklearn.model_selection import RandomizedSearchCV

# load data
cancer = datasets.load_breast_cancer()

# target
y = cancer.target

# features
X = cancer.data

#Instantiate CatBoostClassifier
cbc = CatBoostClassifier()

# Creating the hyperparameter grid
param_dist = { "learning_rate": np.linspace(0,0.2,5)
               "max_depth": randint(3, 10)}
               
#Instantiate RandomSearchCV object
rscv = RandomizedSearchCV(cbc , param_dist, scoring='accuracy', cv =5)

#Fit the model
rscv.fit(X,y)

# Print the tuned parameters and score
print(rscv.best_params_)
print(rscv.best_score_)