Mastering Machine Learning for Penetration Testing

Recursive feature elimination

You can see that recursive feature elimination as a greedy optimization algorithm. This technique is performed by creating models with different subsets and computing the best performing feature, scoring them according to an elimination ranking.

This script is like the previous one, but it uses recursive feature elimination as a feature selection method:

from pandas import read_csvfrom sklearn.feature_selection import RFEfrom sklearn.linear_model import LogisticRegression# load dataurl = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']dataframe = read_csv(url, names=names)array = dataframe.values ...