Base class for the stepwise selection algorithms.
The correlation matrix of all variables. Numpy automatically scales all variables so that each has mean 0 and variance 1, as needed.
This holds all information needed to identify the gains and costs associated with moving features into or out of the predictor set at each step of the process.
This array's index i specifies whether variable i is currently in the
predictor set.
This array's index i specifies whether variable i is free to move in and
out of the predictor set.
This array's index i specifies whether variable i is in the target set,
i.e., one of the variables we are trying to fit.
This is the number of variables, including both the features in X and the
targets in y (when passed).
Computations will be carried out using this level of precision. Note: Lower
precision types result in faster computation. However, for nearly redundant
data sets these can sometimes result in nan results populating the
ordered_cods.