Question - What is entropy in a decision tree algorithm?
Answer -
In a decision tree algorithm, entropy is the measure of impurity or randomness. The entropy of a given dataset tells us how pure or impure the values of the dataset are. In simple terms, it tells us about the variance in the dataset.
For example, suppose we are given a box with 10 blue marbles. Then, the entropy of the box is 0 as it contains marbles of the same color, i.e., there is no impurity. If we need to draw a marble from the box, the probability of it being blue will be 1.0. However, if we replace 4 of the blue marbles with 4 red marbles in the box, then the entropy increases to 0.4 for drawing blue marbles.