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What is Understanding Linear Regression Techniques about?

Linear regression is a method to estimate an unknown function f(x) given input data points. It uses a linear model to fit a straight line to the data that minimizes the error between predicted and actual y-values. Increasing the number of data points or model size can improve the fit, but too large a model may overfit. Regularization adds constraints like L2 normalization to prevent overfitting. Linear regression can handle multiple inputs and outputs as well.

Author

Nitish Deshpande

Language

EN