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In the next step, we calculated Shapley Additive Explanations (SHAP) values for the RF model to understand how each feature contributes to the final prediction. This slide presents the feature contributions in predicting ¡°0¡±. Positive contributions, represented in red, push the prediction toward the outcome ¡°1¡±. Negative contributions, shown in blue, pull the prediction toward the ¡°0¡± outcome. The base value E[f(X)] represents the starting point. The number next to each variable name indicates its value for the specific instance. The number inside the red or blue box denotes the contribution of that variable¡¯s value to the prediction outcome. The value f(x) represents the predicted probability of the outcome based on the model¡¯s function.
For this particular outcome, the likelihood of this client adopting a car loan is 84%. All variables except the female receiver positively contribute to this outcome. A female receiver (value = 1) reduces the likelihood of adoption to 29%. In Figure (b), the likelihood of car loan adoption is 34%. It is positively influenced by remittances from Turkey, a duration (value=2 months), a male receiver, and 22 other features. However, the female sender has the most substantial negative effect on the outcome.
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IEEE/ICACT20250136 Slide.13
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