Calculating goodness of fit measures

Confusion matrix

We can compute the confusion, or error, matrix in order to determine how our manual calculation performed, when we classified the prediction outcomes as correct or not:

#Confusion matrix 
result <- sql("select outcome,correct, count(*) as k, avg(totrows) as totrows from preds_tbl where grp=1 group by 1,2 order by 1,2") 
result$classify_pct <- result$k/result$totrows 

display(result) 

To determine the grand total correct model prediction, sum the correct=Y columns previously:

Summary of correct predictions for training group:

Correctly predicted outcome=1	20%
Correctly predicted outcome=0	59%
Total Correct Percentage	79%

 

You can see that there is much more predictive power in predicting outcome=0 than there is outcome=1.