Probability is a mathematical concept that signifies the uncertainty of an event. It provides a method of calculating uncertainties to derive new uncertain events. In this article, I’ll explain why probability is important for machine learning.
How Probability Is Used in Machine Learning?
Probability is an important concept for machine learning because when building ML applications we use probability in two ways:
- Probability rules tell us how an application should perform so that we can modify our algorithm to give more approximate results by understanding the results expressed by the probability of the accuracy of the trained model.
- We can use both probability and statistics to analyze the behaviour of machine learning applications.
Probability plays a fundamental role in various concepts of science and engineering. In the section below, I’ll explain why probability matters for machine learning.
Why Probability is Important for Machine Learning?
Most areas of computer science deal with completely deterministic and certain entities. A computer programmer simply assumes that the CPU will easily execute every code written by him. This is because hardware errors do occur, but they are rare enough that software applications are not programmed to log the errors.
So computer scientists, software engineers, web developers are working in the same ecosystem, so, surprisingly, only engineers who work on AI applications use probability.
The reason that probability matters for machine learning is that ML applications deal with uncertain amounts of data and sometimes even have to deal with stochastic amounts of data. Here, uncertainty and stochasticity of data can be formed from any reason.
A machine needs a certain ability to reason in times of uncertainty. If we go beyond the machines, it can even be difficult for human beings to think of an event that is sure to happen in the future.
So we need a concept that represents the reasoning of uncertainty. So, the probability is not something that can provide you with all the concepts for handling uncertainties of events in artificial intelligence applications, but to a large extent, probability has been introduced to analyze the frequencies of uncertain events.
So this is how probability plays an important role in ML applications. When we calculate the accuracy of a trained ML model, it means that we are calculating the probability of the model’s performance.
Probability is an important concept of Machine Learning Applications as it can be seen as an extension of logic to deal with uncertainty. A logic provides some rules for determining what propositions can be used to be true or false by assuming the assumptions in a particular situation.
I hope you liked this article on why the probability is important for ML applications. Feel free to ask your valuable questions in the comments section below.