Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems

2016/12/17 by Bohdan M. Pavlyshenko, B. Pavlyshenko, Pavlyshenko, B.
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1612.05740

arxiv created 2016/12/17 · openalex publication_date 2016/12/17 · arxiv updated 2016/12/30 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28

Abstract

In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost tree based classifier to obtain high scored classification. Using the generalized linear model for logistic regression makes it possible to analyze the influence of the factors under study. The Bayesian approach for logistic regression gives the statistical distribution for the parameters of the model. It can be useful in the probabilistic analysis, e.g. risk assessment.

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