Table 2 Building a machine learning based classification device
From: To supervise or to self-supervise: a machine learning based comparison on credit supervision
Steps | Description |
|---|---|
1 | Define the exogenous variables that compound the datasets, known as the matrix of features |
2 | Define the borrowers from which the matrices of features will be built and whose labels (good or bad borrowers, the endogenous variable) are known. In this study, we use two different sets of borrowers, belonging to the two supervisory approaches investigated |
3 | Build the two datasets that will be used to train the algorithms, according to the two supervisory approaches analyzed |
4 | Run (train) the algorithms on the datasets and evaluate their performance |
5 | Build the validation set from the real bank credit portfolio to be classified |
6 | Apply the trained algorithms to the validation set and compare the outcomes |