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  3. Practicing Statistics Interview Questions in Python
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Regression evaluation

Let's revisit the linear regression model that you created with LinearRegression() and then trained with the fit() function a few exercises ago. Evaluate the performance your model, imported here as lm for you to call.

The weather data has been imported for you with the X and y variables as well, just like before. Let's get to calculating the R-squared, mean squared error, and mean absolute error values for the model.

说明 1 / 共 3 个

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  • 1

    Compute and print the R-squared score of our model using the score() function.

  • 2

    Compute and print mean squared error using the mean_squared_error() function.

  • 3

    Adapt your code to compute and print the mean absolute error this time using the mean_absolute_error() function.