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().

Cet exercice fait partie du cours

Introduction to Linear Modeling in Python

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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.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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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