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What is divergence in machine learning?
The Kullback-Leibler divergence (hereafter written as KL divergence) is a measure of how a probability distribution differs from another probability distribution. In this context, the KL divergence measures the distance from the approximate distribution Q to the true distribution P .
What is divergence measure?
In statistics and information geometry, divergence or a contrast function is a function which establishes the “distance” of one probability distribution to the other on a statistical manifold. …
What is KL divergence used for?
To measure the difference between two probability distributions over the same variable x, a measure, called the Kullback-Leibler divergence, or simply, the KL divergence, has been popularly used in the data mining literature. The concept was originated in probability theory and information theory.
Why is KL divergence important in machine learning?
KL Divergence keeps track of reality by helping the user in identifying the differences in data distributions. Since the data handles usually large in machine learning applications, KL divergence can be thought of as a diagnostic tool, which helps gain insights on which probability distribution works better and how far a model is from its target.
How are divergence scores used in machine learning?
Divergence scores are an important foundation for many different calculations in information theory and more generally in machine learning. For example, they provide shortcuts for calculating scores such as mutual information (information gain) and cross-entropy used as a loss function for classification models.
Which is a measure of the divergence between two distributions?
Kullback-Leibler divergence or relative entropy is a measure of the difference between two distributions. Given by the following expression: the reality. The information gain or D KL (p || q), gives the value of information gain if q is used instead of p.
How does Kullback-Leibler divergence relate to KL divergence?
Kullback-Leibler divergence calculates a score that measures the divergence of one probability distribution from another. Jensen-Shannon divergence extends KL divergence to calculate a symmetrical score and distance measure of one probability distribution from another.