- Calculate the following metrics: mean absolute error, mean squared error, root mean
squared error, and the R2 score. Use the following code samples:
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test,
y_pred))
print('Mean Squared Error:', metrics.mean_squared_error(y_test,
y_pred))
print('Root Mean Squared Error:',
np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
print(‘R-squared Score:’, regressor.score(X, y))
Part 2: Model Refinement - Rerun the linear regression model from Module 4, but change the percentage of records
that are used for testing. Try using 0.25 and 0.3. - Calculate the same metrics from above.
- Use a scatter plot to visualize all three models.
- Evaluate the three models. Are any of them underfit or overfit? Which % of testing data
performed best?
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