This code calculates the Mean Squared Error (MSE) and the...

May 12, 2024 at 06:16 PM

predicted = reg.predict(X) mse = ((np.array(y)-predicted)**2).sum()/len(y) r2 = r2_score(y, predicted) print("MSE:", mse) print("R Squared:", r2)

This code calculates the Mean Squared Error (MSE) and the R-squared value for a regression model. It first makes predictions using the regression model reg on the input data X, then calculates the MSE by comparing the predicted values to the actual values y. Finally, it calculates the R-squared value by comparing the actual values y to the predicted values.

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