Confusion matrix

Machine Learning with caret in R

Zach Mayer

Data Scientist at DataRobot and co-author of caret

Confusion matrix

A confusion matrix of prediction versus reference. When prediction and reference are both "yes", you have a true positive. When they are both "no", you have a true negative. When prediction is "yes" and reference is "no", you have a false positive. When prediction is "no" and reference is "yes", you have a false negative.

Confusion matrix

# Fit a model
model <- glm(Class ~ ., family = binomial(link = "logit"), train)
p <- predict(model, test, type = "response")
summary(p)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.9885  0.5296  1.0000  1.0000

# Turn probabilities into classes and look at their frequencies
p_class <- ifelse(p > 0.50, "M", "R")
table(p_class)
p_class

 M  R 
44 39

Confusion matrix

Make a 2-way frequency table
Compare predicted vs. actual classes

# Make simple 2-way frequency table
table(p_class, test[["Class"]])

p_class  M  R
      M 13 31
      R 30  9

Confusion matrix

# Use caret’s helper function to calculate additional statistics
confusionMatrix(p_class, test[["Class"]])

         Reference
Prediction  M  R
         M 13 31
         R 30  9

               Accuracy : 0.2651          
                 95% CI : (0.1742, 0.3734)
    No Information Rate : 0.5181          
    P-Value [Acc > NIR] : 1               

                  Kappa : -0.4731         
 Mcnemar's Test P-Value : 1               

            Sensitivity : 0.3023          
            Specificity : 0.2250          
         Pos Pred Value : 0.2955          
         Neg Pred Value : 0.2308

Let's practice!

Machine Learning with caret in R