How are entropy and information related with decision trees?
Specifically, you learned: Information gain is the reduction in entropy or surprise by transforming a dataset and is often used in training decision trees. Information gain is calculated by comparing the entropy of the dataset before and after a transformation.
How are entropy and information gain related vis a vis decision trees?
Information gain (IG) measures how much “information” a feature gives us about the class. Entropy is the measures of impurity, disorder or uncertainty in a bunch of examples. Entropy controls how a Decision Tree decides to split the data. It actually effects how a Decision Tree draws its boundaries.
What is entropy and information gain in decision trees?
Entropy is an information theory metric that measures the impurity or uncertainty in a group of observations. It determines how a decision tree chooses to split data. The image below gives a better description of the purity of a set. Source. Consider a dataset with N classes.
What is the minimum value of entropy in a Decision Tree and what does it mean?
Entropy by definition is a lack of order or predictability. It is the measure of impurity in a bunch of examples. Entropy is calculated for every feature, and the one yielding the minimum value is selected for the split. The mathematical range of entropy is from 0–1.
What is role of entropy in Decision Tree?
Entropy controls how a Decision Tree decides to split the data. It actually effects how a Decision Tree draws its boundaries.
How are decision trees related to entropy and information gain?
Next we describe several ideas from information theory: information content, entropy, and information gain. Finally we show an example of decision tree learning with the Iris dataset. Decision trees involve a hierarchy of if/else statements. Each if/else node of the tree either terminates with a value or triggers another if/else statement.
How does information gain help the decision tree?
Information gain helps the tree decide which feature to split on: The feature that gives maximum information gain. We’ll now calculate information gain for every feature, one by one. For this iteration, the entropy of the parent node is 1 that we calculated above.
How is entropy related to reduction of uncertainty?
This is where Information Gain comes in. Mathematically it can be written as: We simply subtract the entropy of Y given X from the entropy of just Y to calculate the reduction of uncertainty about Y given an additional piece of information X about Y. This is called Information Gain.
How is conditional entropy related to information gain?
A related important concept is the conditional entropy. Intuitively, this is the expected surprise of the outcome of a random variable , given that we know the outcome of some other random variable . Mathematically it is One metric used to split on is information gain which is also called mutual information.
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