Why are Continuous Probability Distribution important in machine learning?
Imagine trying to build a model to predict the price of a car. You have data on various factors like mileage, year, and brand. But how do you account for the fact that prices can vary continuously? This is where continuous distributions come to the rescue! By fitting a suitable distribution to the price data, you can estimate the probability of a car with specific features falling within a certain price range.
Continuous Probability Distributions for Machine Learning
Machine learning relies heavily on probability distributions because they offer a framework for comprehending the uncertainty and variability present in data. Specifically, for a given dataset, continuous probability distributions express the chance of witnessing continuous outcomes, like real numbers.
Table of Content
- What are Continuous probability distributions?
- Importance in Machine Learning
- Types of Continuous Probability Distributions
- Determining the distribution of a variable