The first number is the total number of instances (weight of instances) reaching the leaf. The second number is the number (weight) of those instances that are misclassified.

If your data has missing attribute values then you will end up with fractional instances at the leafs. When splitting on an attribute where some of the training instances have missing values, J48 will divide a training instance with a missing value for the split attribute up into fractional parts proportional to the frequencies of the observed non-missing values. This is discussed in the Witten & Frank Data Mining book as well as Ross Quinlan's original publications on C4.5.

## What do those numbers mean in a J48 tree?

J48 pruned tree

node-caps = yes

| deg-malig = 1: recurrence-events (1.01/0.4)

| deg-malig = 2: no-recurrence-events (26.2/8.0)

| deg-malig = 3: recurrence-events (30.4/7.4)

node-caps = no: no-recurrence-events (228.39/53.4)

The

firstnumber is the total number of instances (weight of instances) reaching the leaf. Thesecondnumber is the number (weight) of those instances that are misclassified.If your data has

missingattribute values then you will end up withfractionalinstances at the leafs. When splitting on an attribute where some of the training instances have missing values, J48 will divide a training instance with a missing value for the split attribute up into fractional parts proportional to the frequencies of the observed non-missing values. This is discussed in the Witten & Frank Data Mining book as well as Ross Quinlan's original publications on C4.5.Taken from the following Wekalist posts: