{'fraction_non_alphanumeric': 0.020634920634920634, 'fraction_numerical': 0.0, 'mean_word_length': 5.6421052631578945, '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}}
Evaluating parallel logistic regression models

Logistic regression (LR) has been widely used in applications of machine learning, thanks to its linear model. However, when the size of training data is very large, even such a linear model can consume excessive memory and computation time. To tackle both resource and computation scalability in a big-data setting, we evaluate and compare different approaches in distributed platform, parallel algorithm, and sublinear approximation. Our empirical study provides design guidelines for choosing the most effective combination for the performance requirement of a given application.