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

TL;DR

This paper compares machine learning, linear, and Bayesian logistic regression models for manufacturing failure detection using Kaggle data, highlighting their respective advantages in classification accuracy, interpretability, and probabilistic analysis.

Contribution

It introduces a comprehensive analysis of different logistic regression approaches, including Bayesian methods, for failure detection in manufacturing.

Findings

XGBoost achieves high classification scores.

Linear models facilitate factor influence analysis.

Bayesian models provide parameter distributions for risk assessment.

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.