{'fraction_non_alphanumeric': 0.018779342723004695, 'fraction_numerical': 0.0, 'mean_word_length': 5.336633663366337, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Wrapper consistency analysis: a measure of consistency in a wrapper setting.

Typically, in high-throughput data, the number of features is often substantially greater than the number of samples. One approach to this statistical challenge is to perform feature selection and usually, only predictive accuracy is used to perform feature selection. We present a new feature selection method called Wrapper Consistency Analysis that strives to optimize both the predictive accuracy as well as a measure called overlap (or consistency). We believe this analysis provides additional information about the goodness of a selected set of features.