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".
Diese Übung ist Teil des Kurses
Introduction to Linear Modeling in Python
Anleitung zur Übung
- Load the
x_data,y_datawith the pre-definedload_data()function. - Call the pre-defined
model(), passing inx_dataand specific valuesa0,a1. - Compute the residuals as
y_data - y_modeland then findrssby usingnp.square()andnp.sum(). - Print the resulting value of
rss.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# 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(____))