IMPLEMENTATION OF EXACT MACHINE UNLEARNING ALGORITHM IN CREDIT SCORING CASES
Machine unlearning (MUL) is an approach used to remove the influence of training data on a machine learning model. This removal can be necessary for various reasons, such as privacy protection, security enhancement, usability optimization, and improving model accuracy. This approach aims to produ...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/85032 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Machine unlearning (MUL) is an approach used to remove the influence of training
data on a machine learning model. This removal can be necessary for various
reasons, such as privacy protection, security enhancement, usability optimization,
and improving model accuracy. This approach aims to produce a model whose
performance is similar to that of a retrained model (naïve MUL). This study aims
to evaluate the performance differences between naïve machine unlearning and
using exact machine unlearning algorithms, specifically the SISA and DaRE Forest
algorithms, in a credit scoring scenario.
The credit scoring models were developed using the random forest algorithm as the
base model. The MUL algorithms, SISA and DaRE Forest, were also developed
using the random forest base model to remove the influence of k = 50% of the
training data. The evaluation was conducted by analyzing the model accuracy, the
time required to retrain the model, and the similarity of prediction outcomes from
each model.
The results show that the DaRE Forest algorithm outperforms the SISA and naïve
models. The model produced by DaRE Forest achieved higher accuracy, faster
retraining times, and greater similarity in prediction outcomes. |
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