Regression is a supervised learning where the goal is to predict a continuous output variable based on the input features.
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The input features are typically represented as a vector of numerical values.
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The model is trained on the training set and evaluated on the validation set to check its performance.
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The performance of the model is typically measured using metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared.
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There are many types of regression models like linear,non linear and Polynomial regression model.
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Decision trees and random forests are non-linear regression algorithms.
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SVR is a powerful and versatile regression algorithm that can handle both linear and non-linear problems.
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Regression has many practical applications, including stock price prediction, sales forecasting, and medical diagnosis.
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What is Classification in Machine Learning? A Beginner’s Guide