Probabilistic Models in ML
- (The University of Chicago - Alvin Wei-Cheng Wong)
- Overview
Probabilistic models in machine learning (ML) are popular algorithms used in ML. It is a combination of discriminant analysis and multinomial Bayesian classifier. Probabilistic models in ML learn from data more efficiently than traditional statistical techniques.
A probabilistic model is a ML method in which decisions are made by using the probabilities of possible outcomes of independent variables and the assumption that the likelihood of certain events is constant.
For example, it can be used to make the best choice among multiple alternatives. The main advantage of this model is that it relies on an underlying learning algorithm that uses simple rules, such as taking action if the expected value is positive, or taking action if the expected value exceeds a certain threshold.
[More to come ...]
