MachineLearning -

Note: Some of the methods would be deprecated. Please replace them with the latest keywords/methods available. Care has been taken to update the methods as per the latest version available.

Various Machine Learning Techniques with Problem description and solution are discussed here.

Simple Regression:

Simple regression considers the relation between a single explanatory variable and response variable and fits a regression line in 2-dimensional space. Simple Regression coefficients are estimated by minimizing ∑residuals2 (i.e., sum of the squared residuals) to derive this model: y = a+bx

Linear Regression:

Multiple regression simultaneously considers the influence of multiple explanatory variables on a response variable Y. The main purpose of the best fit line is that our predicted values should be closer to our actual or the observed values. The difference between the observed value of the dependent variable (y) and the predicted value (ŷ) is called the residual (e). Ordinary Least Squares (OLS) regression is a linear model that seeks to find a set of coefficients for a line/hyper-plane that minimize the sum of the squared errors.