logistic regression, a guide

 

https://medium.com/analytics-vidhya/a-comprehensive-guide-to-logistic-regression-e0cf04fe738c

decision boundary=cut off =threshold 

time series analysis, lags and autocorrelations

 

https://www.business-science.io/timeseries-analysis/2017/08/30/tidy-timeseries-analysis-pt-4.html

guide to time series analysis

 

https://towardsdatascience.com/the-complete-guide-to-time-series-analysis-and-forecasting-70d476bfe775

bidding on click through, cost per impressions, conversion

 facebook , bidding on click through, cost per impressions, conversion? 

how to choose from the prices? bidding 

choose the cut off for binary classification

 

http://ethen8181.github.io/machine-learning/unbalanced/unbalanced.html#choosing-the-suitable-cutoff-value

why most of the time , the cut off or threshold is 0.5?

https://www.graphpad.com/guides/prism/latest/curve-fitting/reg_logistic_roc_curves.htm

in the built-in function, the cut off is 0.5

when reporting the confusion matrix, can change the cut off to an arbituray number, and the confusion matrix will be changed accordingly, when changing the cut off, the false positive rate and false negative rate will change accordingly. 

dat <- iris dat$positive <- as.factor(ifelse(dat$Species == "setosa", "s", "ns")) library(caret) mod <- train(positive~Sepal.Length, data=dat, method="glm")

confusionMatrix(table(predict(mod, type="prob")[,"s"] >= 0.25,
                      dat$positive == "s"))
# Confusion Matrix and Statistics
# 
#        
#         FALSE TRUE
#   FALSE    88    3
#   TRUE     12   47
#                                           
#                Accuracy : 0.9             
#                  95% CI : (0.8404, 0.9429)
#     No Information Rate : 0.6667          
#     P-Value [Acc > NIR] : 2.439e-11       
#                                           
#                   Kappa : 0.7847          
#  Mcnemar's Test P-Value : 0.03887         
#                                           
#             Sensitivity : 0.8800          
#             Specificity : 0.9400          
#          Pos Pred Value : 0.9670          
#          Neg Pred Value : 0.7966          
#              Prevalence : 0.6667          
#          Detection Rate : 0.5867          
#    Detection Prevalence : 0.6067          
#       Balanced Accuracy : 0.9100