Key points about Linear Regression:
It is a simple and commonly used machine learning algorithm for predicting continuous or numeric outcomes.
It works by finding a linear relationship between two or more variables – a dependent variable and one or more independent variables.
The relationship is modeled using a straight line or linear equation of the form y = mx + c, where m is the slope and c is the y-intercept.
It assumes a linear relationship between dependent and independent variables and finds the coefficients that minimize the prediction errors using techniques like Gradient Descent.
The coefficients indicate the extent of influence of each independent variable on the dependent variable.
It can be used for both explanatory (understanding variables’ influence) and predictive (forecasting new outputs) analytics problems.
Common metrics to evaluate the fit of the linear model include R-squared, RMSE, MAE, MAPE etc.
Regularization techniques like Ridge and Lasso can help address overfitting due to many features.
Many variations exist like Logistic Regression, Multi-variat Linear Regression, Polynomial Regression etc.
So in summary, Linear Regression establishes a linear relationship between variables for prediction and explanation tasks.
