Tag: regression

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. Continue reading Machine Learning and Credit Risk (part 6) – Multi-class Linear Discriminate Analysis

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. Continue reading Machine Learning and Credit Risk (part 5) – Neural Networks

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. Continue reading Machine Learning and Credit Risk (part 4) – Support vector Machines

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. Continue reading Machine Learning and Credit Risk (part 3) – Multinomial Logistic Regression