Core Concepts in AI, Machine Learning, and Industrial Automation Systems
Linear Regression Fundamentals
In regression, a set of records containing X and Y values is used to learn a function. This learned function can then be used to predict Y from an unknown X. In regression, we aim to find the value of Y, so a function is required which predicts Y given X. Y is continuous in the case of regression.
Here, Y is called the criterion variable and X is called the predictor variable. There are many types of functions or models which can be used for regression. The linear function is the simplest type of function. Here, X may be a single feature or multiple features representing the problem.
Applications of Linear Regression in AI
- Predictive Analysis: Forecasting sales, stock prices, or house prices based on historical data.
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