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Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems B. Pavlyshenko (Ph.D.) SoftServe, Inc., Ivan Franko National University of Lviv, Lviv,Ukraine

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Page 1: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

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

B. Pavlyshenko (Ph.D.)SoftServe, Inc., Ivan Franko National University of Lviv, Lviv,Ukraine

Page 2: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

MACHINE LEARNING MODELThe most important features and their gain values:

Matthews correlation coefficient (MCC) :

Page 3: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

MACHINE LEARNING MODEL

ROC curve for classification resultsAUC=0.753

Matthews correlation coefficient for logistic regression for different values of probability threshold.

Page 4: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

Matthews correlation coefficient for different samples sets 

MACHINE LEARNING MODEL

Page 5: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

ROC curve and Matthews correlation coefficient for different sets of features

MACHINE LEARNING MODEL

Features set 1:AUC=0.75

Features set 2:AUC=0.91

Page 6: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

MULTILEVEL MODEL

Page 7: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

GENERALIZED LINEAR MODEL

Dependence of total within-clusters sum of squares from number of clusters.

Page 8: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

Dependence of Lambda from AUC value.

Coefficients of the generalized linear model for logistic regression (Lambda=0.03 )

GENERALIZED LINEAR MODEL

Page 9: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

GENERALIZED LINEAR MODEL

Histograms, correlation coefficients, pairs scatterplots for features.

Page 10: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

BAYESIAN MODEL

model{ for (i in 1:n) { y[i] ~ dbern(p[i]) logit(p[i]) <- b0+inprod(b[ ],x[i,]) } b0 ~ dnorm(0,0.0001) for (j in 1:nfeat) { b[j] ~ dnorm(0,0.0001) }}

Probabilistic model for logistic regression using BUGS syntax

Page 11: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

BAYESIAN MODEL

Trace plot for Intercept parameter. Probability density function for Intercept parameter.

Page 12: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

BAYESIAN MODEL

Box plots for logistic regression coefficients.

Page 13: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

Combining Machine Learning withLinear and Bayesian Models

Page 14: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

Combining Machine Learning with Linear Model

Parameters set 1:max.depth = 15, colsample_bytree = 0.7

Parameters set 2:max.depth = 5, colsample_bytree = 0.7

Parameters set 3:max.depth = 15, colsample_bytree = 0.3

Matthews correlation coefficient for different XGBoost parameter sets (features set 2):

Matthews correlation coefficient for different XGBoost parameter sets (features set 1):

Page 15: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

Combining Machine Learning with Bayesian Model

Page 16: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

Study of Reliability of PartsWeibull distribution

Page 17: PresentationMachine Learning, Linear and Bayesian Models  for Logistic Regression in Failure Detection Problems

Thank you for your attention !

Special thanks to Bosch company for awarding me the travel grant for attending the IEEE BigData

2016 conference !