{'fraction_non_alphanumeric': 0.02334384858044164, 'fraction_numerical': 0.0012618296529968455, 'mean_word_length': 5.635983263598327, '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}}
On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers

Linear models in machine learning are extremely computational efficient but they have high representation bias due to non-linear nature of many real-world datasets. In this article, we show that this representation bias can be greatly reduced by discretization. Discretization is a common procedure in machine learning that is used to convert a quantitative attribute into a qualitative one. It is often motivated by the limitation of some learners to handle qualitative data. Since discretization looses information (as fewer distinctions among instances are possible using discretized data relative to undiscretized data) – where discretization is not essential, it might appear desirable to avoid it, and typically, it is avoided. However, in the past, it has been shown that discretization can leads to superior performance on generative linear models, e.g., naive Bayes. This motivates a systematic study of the effects of discretizing quantitative attributes for discriminative linear models, as well. In this article, we demonstrate that, contrary to prevalent belief, discretization of quantitative attributes, for discriminative linear models, is a beneficial pre-processing step, as it leads to far superior classification performance, especially on bigger datasets, and surprisingly, much better convergence, which leads to better training time. We substantiate our claims with an empirical study on 52 benchmark datasets, using three linear models optimizing different objective functions.