Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems
This work addresses failure detection in manufacturing, but it appears incremental as it applies standard methods without novel breakthroughs.
The study applied logistic regression with machine learning, linear, and Bayesian models to detect manufacturing failures using the Bosch Production Line Performance dataset, but did not report specific performance numbers or results.
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.