How to Explain Key Machine Learning Algorithms at an Interview - KDnuggets

Linear Regression involves finding a'line of best fit' that represents a dataset using the least squares method. The least squares method involves finding a linear equation that minimizes the sum of squared residuals. A residual is equal to the actual minus predicted value. To give an example, the red line is a better line of best fit than the green line because it is closer to the points, and thus, the residuals are smaller. Ridge regression, also known as L2 Regularization, is a regression technique that introduces a small amount of bias to reduce overfitting.