Scores Features for Feature Selection
Description
For each feature, a score is computed that can be useful
for feature selection. Several random subsets are sampled from
the input data and for each random subset, various linear
models are fitted using lars method. A score is assigned to
each feature based on the tendency of LASSO in including that
feature in the models.Finally, the average score and the models
are returned as the output. The features with relatively low
scores are recommended to be ignored because they can lead to
overfitting of the model to the training data. Moreover, for
each random subset, the best set of features in terms of global
error is returned. They are useful for applying Bolasso, the
alternative feature selection method that recommends the
intersection of features subsets.