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

被引：0

作者：

Pavlyshenko, B. ^{[1
]}

机构：

[1] Ivan Franko Natl Univ Lviv, SoftServe Inc, Lvov, Ukraine

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2016年

关键词：

logistic regression; XGBoost; Bayesian inference; failure detection;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2046 / 2050

页数：5

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