Logistic Regression Versus Random Forest

In this chapter, we will be making a comparison between logistic regression and random forest, with a classification example of German credit data. Logistic regression is a very popularly utilized technique in the credit and risk industry for checking the probability of default problems. Major challenges nowadays being faced by credit and risk departments with regulators are due to the black box nature of machine learning models, which is slowing down the usage of advanced models in this space. However, by drawing comparisons of logistic regression with random forest, some turnarounds could be possible; here we will discuss the variable importance chart and its parallels to the p-value of logistic regression, also we should not forget the major fact that significant variables remain significant in any of the models on a fair ground, though some change in variable significance always exists between any two models.