التفاصيل البيبلوغرافية
| العنوان: |
Explainable AI and Interpretable Model for Insurance Premium Prediction. |
| المؤلفون: |
Isa, Umar Abdulkadir, Fernando, Anil |
| المصدر: |
Journal of Artificial Intelligence (2579-0021); 2023, Vol. 5, p31-42, 12p |
| مصطلحات موضوعية: |
MACHINE learning, ARTIFICIAL intelligence, INSURANCE premiums, RANDOM forest algorithms, TECHNOLOGICAL innovations |
| مستخلص: |
Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%-92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%-98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating a model-independent, locally accurate explanation set that makes it better than the primary model. As we know that human interaction with machine learning systems on this model's interpretability is more crucial. For supporting set validations in model selection insurance premium prediction. In this study, we proposed the use of the (LIME and SHAP) approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums. The SHAP algorithm's drawback, as seen in our experiments, is its lengthy computing time--to produce the findings, it must compute every possible combination. In addition, the experiments conducted were intended to focus on the model's interpretability and explain its ability using LIME and SHAP, not the insurance premium charge prediction. Three experiments were conducted through experiment, one was to interpret the random forest regression model using LIME techniques. In experiment 2, we used the SHAP technique to interpret the model insurance premium prediction (IPP). [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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