Information gain formula

Information Gain is calculated for a split by subtracting the weighted entropies of each branch from the original entropy. When training a Decision Tree using these metrics, the best split is chosen by maximizing Information Gain.

1. What is information gain and entropy?
2. How do you calculate information?
3. How do you calculate entropy gain?
4. How do you calculate gain ratio in data mining?
5. What is information gain in data mining?
6. Can information gain be greater than 1?
7. How does Python calculate information gain?
8. What is the range of information gain?
9. Why log2 is used in entropy?
10. What is entropy by Shannon?
11. How do you prune a decision tree?

What is information gain and entropy?

The information gain is the amount of information gained about a random variable or signal from observing another random variable. Entropy is the average rate at which information is produced by a stochastic source of data, Or, it is a measure of the uncertainty associated with a random variable.

How do you calculate information?

We can calculate the amount of information there is in an event using the probability of the event. This is called “Shannon information,” “self-information,” or simply the “information,” and can be calculated for a discrete event x as follows: information(x) = -log( p(x) )

How do you calculate entropy gain?

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. The greater the reduction in this uncertainty, the more information is gained about Y from X.

How do you calculate gain ratio in data mining?

First, determine the information gain of all the attributes, and then compute the average information gain. Second, calculate the gain ratio of all the attributes whose calculated information gain is larger or equal to the computed average information gain, and then pick the attribute of higher gain ratio to split.

What is information gain in data mining?

Information gain is the reduction in entropy or surprise by transforming a dataset and is calculated by comparing the entropy of the dataset before and after a transformation.

Can information gain be greater than 1?

Yes, it does have an upper bound, but not 1. The mutual information (in bits) is 1 when two parties (statistically) share one bit of information.

How does Python calculate information gain?

Partition the dataset based on unique values of the descriptive feature. Compute impurity for each partition. Compute the remaining impurity as the weighted sum of impurity of each partition. Compute the information gain as the difference between the impurity of the target feature and the remaining impurity.

What is the range of information gain?

The next step is to find the information gain (IG), its value also lies within the range 0–1. Information gain helps the tree decide which feature to split on: The feature that gives maximum information gain.

Why log2 is used in entropy?

If the base of the logarithm is b, we denote the entropy as Hb(X). If the base of the logarithm is e, the entropy is measured in nats. Unless otherwise specified, we will take all logarithms to base 2, and hence all the entropies will be measured in bits.

What is entropy by Shannon?

Meaning of Entropy

At a conceptual level, Shannon's Entropy is simply the "amount of information" in a variable. More mundanely, that translates to the amount of storage (e.g. number of bits) required to store the variable, which can intuitively be understood to correspond to the amount of information in that variable.

How do you prune a decision tree?

We can prune our decision tree by using information gain in both post-pruning and pre-pruning. In pre-pruning, we check whether information gain at a particular node is greater than minimum gain. In post-pruning, we prune the subtrees with the least information gain until we reach a desired number of leaves.