RMSE Step-by-step

In this exercise, you will quantify the over-all model "goodness-of-fit" of a pre-built model, by computing one of the most common quantitative measures of model quality, the RMSE, step-by-step.

Start with the pre-loaded data x_data and y_data, and use it with a predefined modeling function model_fit_and_predict().

This exercise is part of the course

Introduction to Linear Modeling in Python

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Exercise instructions

Compute y_model values from model_fit_and_predict(x_data, y_data).
Compute the residuals as the difference between y_model and y_data.
Use np.sum() and np.square() to compute RSS, and divide by len(residuals) to get MSE.
Take the np.sqrt() of MSE to get RMSE, and print all results.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Build the model and compute the residuals "model - data"
y_model = model_fit_and_predict(x_data, y_data)
residuals = ____ - ____

# Compute the RSS, MSE, and RMSE and print the results
RSS = np.____(np.____(residuals))
MSE = ____/len(residuals)
RMSE = np.____(____)
print('RMSE = {:0.2f}, MSE = {:0.2f}, RSS = {:0.2f}'.format(RMSE, MSE, RSS))

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