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Residual Sum of the Squares

In a previous exercise, we saw that the altitude along a hiking trail was roughly fit by a linear model, and we introduced the concept of differences between the model and the data as a measure of model goodness.

In this exercise, you'll work with the same measured data, and quantifying how well a model fits it by computing the sum of the square of the "differences", also called "residuals".

Acest exercițiu face parte din cursul

Introduction to Linear Modeling in Python

Vezi cursul

Instrucțiuni pentru exercițiu

  • Load the x_data, y_data with the pre-defined load_data() function.
  • Call the pre-defined model(), passing in x_dataand specific values a0, a1.
  • Compute the residuals as y_data - y_model and then find rss by using np.square() and np.sum().
  • Print the resulting value of rss.

Exercițiu interactiv practic

Încearcă acest exercițiu completând acest cod de exemplu.

# Load the data
x_data, y_data = load_data()

# Model the data with specified values for parameters a0, a1
y_model = model(____, a0=150, a1=25)

# Compute the RSS value for this parameterization of the model
rss = np.sum(np.square(____ - ____))
print("RSS = {}".format(____))
Editează și rulează codul