Performance metrics in Classification and Regression
In this post, we will look at different performance metrics for classification and regression. For regression, metrics are Mean absolute error, Mean squared error, R-Square and many more. For classification, the metrics are accuracy, precision, recall and many more.
Principal Component Regression (PCR)
Principal Component Regression (PCR) is an algorithm for reducing the multi-collinearity of a dataset. PCR is basically using PCA, and then performing Linear Regression on these new PCs. The key idea of how PCR aims to do this, is to use PCA on the dataset before regression.
Ridge regression is an efficient regression technique that is used when we have multicollinearity or when the number of predictor variables in a set exceed the number of observations. It uses L2 regularization and solves the problem of overfitting. Concepts of overfitting and regularization is basis