Linear Discriminate Analysis (LDA) is another method which should be familiar to statisticians and economists.  LDA is a dimensionality reduction technique which has found its use in machine learning because of how well it functions as a classifier. Its primary goal is to project data onto a lower dimensional space.Read More »

Neural networks models are a flexible class of machine learning algorithms which can be used for both supervised as well as unsupervised learning and can approximate discrete or continuous functions.  They are loosely modeled on the functions of human brains and attempt to allow computers to learn in manner similar to humans.Read More »

Support Vector Machines (SVM) algorithms are some of the best “out-of-box” machine learning tools available. They are not only used for both linear and nonlinear classifications but can also be extended from binary classification to support multi-class classification.Read More »

Logistic regression has been a reliable tool in many Statisticians/Economists toolkit for many years when dealing with binary problems where the output is 0/1, True/False, or any variation of a dichotomous problem. But the reality is that Multinomial Logistic regression is a very important ‘algorithm’ in the machine learning sphere.Read More »

The canonical method to forecasting a credit migration matrix is an econometric model: the one factor approach described in Belkin et al. (1998). This approach suggests that one might consider an approach to condition migration (transition) matrices by creating a systematic component which represents the “credit cycle” that relates the economic condition to the credit quality of a loan portfolio.  The credit cycle can be thought of as the historical pattern of credit rating shared by all borrowers in a sector or economy.Read More »