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Github link: https://github.com/Jagadeesh-Surendran/python-_machine_problems/blob/main/Problem_3%3A%20Implement%20k-fold%20cross-validation%20using%20Scikit-learn.
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#pythoncode :
from sklearn.model_selection import KFold
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import numpy as np
# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 3, 3, 2, 5])
# K-fold cross-validation
kf = KFolds(n_splits=3)
model = LinearRegression()
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model.fit(X_train, y_train)
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'MSE: {mse}')
Explanation: This code performs k-fold cross-validation on a linear regression model, printing the mean square error for each fold.
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