Implementing feature binarization

Some datasets contain sparse variables. Sparse variables are those where the majority of the values are 0. The classical example of sparse variables are those derived from text data through the bag-of-words model, where each variable is a word and each value represents the number of times the word appears in a certain document. Given that a document contains a limited number of words, whereas the feature space contains the words that appear across all documents, most documents, that is, most rows, will show a value of 0 for most columns. However, words are not the sole example. If we think about house details data, the number of saunas variable will also be 0 for most houses. In summary, some variables have very skewed distributions, where most observations show the same value, usually 0, and only a few observations show different, usually higher, values.

For a simpler representation of these sparse or highly skewed variables, we can binarize them...