how to do hyperparameter tuning using K Fold cross-validation

[maruf78901]

import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
df = pd.read_csv(r"C:\Users\mdmar\Downloads\Thesis\Data/1024.csv")
#print(df.head())
#sizes = df['target'].value_counts(sort=1)
#print(sizes)
#Define dependent variable
Y = df['target'].values
Y=Y.astype('int')
#define independent variable
X = df.drop(labels=['target'], axis=1)
#Split dataset for train and test
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test=train_test_split(X,Y,test_size=0.30, random_state=30)
#print(X_train)
#Import Random Forest
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=70, random_state=40)
model.fit(X_train,Y_train)
prediction_test = model.predict(X_test)
print(prediction_test)
from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(Y_test, prediction_test)*100,'%')
print(model.feature_importances_)
print(model.feature_importances_*100,'%')
Out : [1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 1 1 1 1 1 0 1 0 1 0 1 1
1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 1 0 0
0 0 1 1 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 0 0
0 0 1 0 0 0 0 1 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 0 1 1
0 0 0 0 1 0 1 0 0 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0
0 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 0
0 0 0 1 0 0 1 1 1 1 1 1 0 1 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0
1 1 1 1 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 0 1 1
1 0 0 1 0 1 0 0 1 1 1 1]
Accuracy: 97.72727272727273 %
[0.1031516 0.0358877 0.11881306 0.09649581 0.09640051 0.01116345
0.02173903 0.13786201 0.06464796 0.13677327 0.05528801 0.12177758]
[10.31516006 3.58876957 11.88130644 9.64958137 9.64005136 1.11634483
2.17390296 13.78620058 6.46479635 13.67732725 5.52880149 12.17775775] %

how to do hyperparameter tuning using K Fold cross-validation using this model

[Tusharbagul]

You can probably try following code,

from sklearn.model_selection import cross_val_score
cross_val_score(model, X, Y, cv=3)