Master Linear Regression in Machine Learning

Understanding Linear Regression in Machine Learning

• Linear Regression is a supervised learning algorithm used for regression tasks to model a target prediction value based on independent variables.
• Regression models are used to find out the relationship between variables and forecasting.
• The dependent variable in regression can be called an outcome variable, criterion variable, endogenous variable, or regressand, while the independent variables can be called exogenous variables, predictor variables, or regressors.
• Linear regression is used in various fields, including finance, economics, and psychology to understand and predict the behavior of a variable.
• The regression line is the best-fit line for the model, and the hypothesis function for linear regression is to predict the value of y for a given value of x.
• The model finds the best regression fit line by finding the best θ1 and θ2 values, where θ1 is the intercept and θ2 is the coefficient of x.
• Linear regression assumes a linear relationship between the independent and dependent variables, which may not always be the case.
• Linear regression is sensitive to outliers, which can have a disproportionate effect on the fitted line, leading to inaccurate predictions.
• The cost function (J) of Linear Regression is the Root Mean Squared Error (RMSE) between predicted y value (pred) and true y value (y).
• Gradient Descent is used to update θ1 and θ2 values in order to reduce the cost function (minimizing RMSE value) and achieving the best-fit line.
• Linear regression is a powerful tool for understanding and predicting the behavior of a variable.
• Linear regression can be used with a single or multiple features representing the problem.