How to get validation rms in sklearn model

This recipe helps you get validation rms in sklearn model. Scikit Learn provides mean squared error function in order to find out the mean square error.

Recipe Objective - How to get validation RMSE in sklearn model?

The Mean squared error regression loss.

Scikit-Learn proides "sklearn.metrics.mean_squared_error()" function in order to find out the mean square error.

The mean_squared_error function computes mean square error, a risk metric corresponding to the expected value of the squared error or loss.

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Example:- 01

from sklearn.metrics import mean_squared_error

y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_squared_error(y_true, y_pred)

0.375

Example:- 02

y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_squared_error(y_true, y_pred, squared=False)

0.6123724356957945

Example:- 03

y_true = [[0.5, 1],[-1, 1],[7, -6]]
y_pred = [[0, 2],[-1, 2],[8, -5]]
mean_squared_error(y_true, y_pred)

0.7083333333333334

mean_squared_error(y_true, y_pred, squared=False)

0.8416254115301732

mean_squared_error(y_true, y_pred, multioutput='raw_values')

array([0.41666667, 1.        ])

mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])

0.825

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