Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
