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203.2.4 Goodness of fit for Logistic Regression

Goodness of Fit for a Logistic Regression

In previous section, we studied about Multiple Logistic Regression

  • Classification Matrix
  • Accuracy

Classification Table & Accuracy

Predicted / Actual 0 1
0 True Positive (TP) False Positive (FP)
1 False Negative (FN) True Negative (TN)
  • Also known as confusion matrix
  • \(Accuracy=\frac{(TP+TN)}{(TP+FP+FN+TN)}\)

Classification Table in R

threshold=0.5
predicted_values<-ifelse(predict(prod_sales_Logit_model,type="response")>threshold,1,0)
actual_values<-prod_sales_Logit_model$y

conf_matrix<-table(predicted_values,actual_values)
conf_matrix
##                 actual_values
## predicted_values   0   1
##                0 257   3
##                1   5 202

Accuracy in R

accuracy<-(conf_matrix[1,1]+conf_matrix[2,2])/(sum(conf_matrix))
accuracy
## [1] 0.9828694

 

The next post is about multi collinearity an individual impact of variables in logistic regression.

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