The Friedman-1 regression problem, which was created by Friedman and Breiman, describes a single output value, y, which is a function of five input values, x0..x4, and randomly generated noise, according to the following formula:
The input variables, x0..x4, are independent, and uniformly distributed over the interval [0, 1]. The last component in the formula is the randomly generated noise. The noise is normally distributed and multiplied by the constant noise, which determines its level.
In Python, the scikit-learn (sklearn) library provides us with the make_friedman1() function, which can be used to generate a dataset containing the desired number of samples. Each of the samples consists of randomly generated x0..x4 values and their corresponding calculated y value. The interesting part, however, is that we can tell...
