Problem_3: Implement k-fold cross-validation using Scikit-learn #upgrade2python #ai #coding #py

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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 = KFold(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 squared error for each fold.



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Mots-clés
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