When we assign equal weightage to the rows that belong to a defaulter and the rows that belong to a non-defaulter, potentially the model can fine-tune for the non-defaulters. In this section, we will look into ways of assigning a higher weightage so that our model classifies defaulters better.
Assigning weights for classes
Getting ready
In the previous section, we assigned the same weightage for each class; that is, the categorical cross entropy loss is the same if the magnitude of difference between actual and predicted is the same, irrespective of whether it is for the prediction of a default or not a default.
To understand the scenario further, let's consider the following example:
| Scenario | Probability of default... |
