Decision tree: how you would expect the next split based on a set of variables? I'm trying to understand the logic behind a question I was given during a mock test. Can somebody help me please? I am not sure I can understand the concept, hence be able to make it right in a similar problem.
Based on the information below, on which attribute would you expect the next split to be in the decision tree?

Variable, Info-Gain
Var A,    0.0310
Var B,    0.0100
Var C,    0.0034
Var D,    0.0456

Thanks
 A: Var D.
Information gain is one of the main metrics used by decision tree learning, others being Gini impurity and variance reduction. The Wikipedia page explains everything below:
Information gain is based on the concept of entropy and information content from information theory.
Consider an example data set with four attributes: outlook (sunny, overcast, rainy), temperature (hot, mild, cool), humidity (high, normal), and windy (true, false), with a binary (yes or no) target variable, play, and 14 data points. To construct a decision tree on this data, we need to compare the information gain of each of four trees, each split on one of the four features. The split with the highest information gain will be taken as the first split and the process will continue until all children nodes are pure, or until the information gain is 0.
A: A decision tree chooses, at each node, the features by each to split the observations based on the following concept:

"Which feature of the dataset allows the division of the observations in a way that the resulting groups are as different as possible, and that the members of each group are as similar as possible?"

Therefore, since you are given the "Info-gain" (meaning, the bigger the info-gain, the better the variable chosen for splitting in that node), I would say that the chosen variable is the one with the biggest info-gain, since your objective is, in each node, to gain as many information about the data as possible (in this case, variable D).
