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Machine Learning (ML) and Deep Learning models are used to predict default probabilities in the credit market, such as mortgages, consumer utility payment performance, consumption loans, and small business loans. The random forest algorithm is the most commonly used model and works by first searching for the single loan-to-value (LTV) value that best separates defaulters from non-defaulters, and then implementing bootstrap aggregation or “bagging” techniques to address the overfitting problem.
