# How do decision trees in random forests handle conflicts?

Let's say our input elements (training data) are 6 people with three attributes, Height, Weight, and Gender, and we are predicting if that person will have cancer or not (boolean 0 or 1). Let's say we want to create 2 decision trees in our random forest, each tree containing 3 people. Now let's analyze the 3 people in one of these trees.

Person ID: 0
Height: 170cm
Weight: 70kg
Gender: Male
Cancer?: Yes

Person ID: 1
Height 150cm
Weight: 55kg
Gender: Female
Cancer? No

Person ID: 2
Height: 170cm
Weight: 70kg
Gender: Male
Cancer?: No


We have a conflict, because both Person 0 and Person 2 have the same attributes for Height, Weight, and Gender, but Person 0 has cancer, but Person 2 does not.

How does the decision tree creation algorithm (within the context of random forests) handle this?

• No split improves the loss, so they are kept together (and act as a leaf with high loss). Jul 21, 2020 at 5:58
• unclear regarding what you just said