Master Linear Regression in Machine Learning

Study Notes

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.

Description

Test your knowledge of Linear Regression in Machine Learning with this quiz! From understanding the basics of regression models to identifying the assumptions and limitations of linear regression, this quiz covers it all. Challenge yourself to see how well you understand the concepts of linear regression, including the cost function, gradient descent, and best-fit line. Perfect for beginners and experts alike, this quiz will assess your understanding of linear regression and help you improve your machine learning skills.