sklearn mse in python

You can use the mean_squared_error function from the sklearn.metrics module in scikit-learn to calculate the mean squared error (MSE).

Here's an example of how to use it:

main.py
from sklearn.metrics import mean_squared_error

# true values
y_true = [3, -0.5, 2, 7] 

# predicted values
y_pred = [2.5, 0.0, 2, 8] 

# calculate mean squared error
mse = mean_squared_error(y_true, y_pred)

print("Mean Squared Error:", mse)
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This will output:

main.py
Mean Squared Error: 0.375
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In this example, y_true represents the true values and y_pred represents the predicted values. The mean_squared_error function compares these two arrays and calculates the mean squared error between them.

Please make sure you have scikit-learn installed (pip install scikit-learn) before running this code.

Hope this helps!