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Machine Learning model interpretability using SHAP values: Application to Igneous Rock Classification task

El Fierro intrusive body is one of the bodies that compose the La Jovita–Las Aguilas mafic–ultramafic belt, located in the Sierra Grande de San Luis, Argentina. The units of this belt carry a base metal sulfide (BMS) mineralization and platinum group minerals (PGM). The macroscopic description of mafic and ultramafic rocks, as is usually done by the mining exploration companies, leads to an imprecise modal classification of the rocks. In this study, we develop a random forest-based prediction model, which uses geochemical parameters to classify mafic and ultramafic rocks intercepted by drill cores. This model showed an accuracy of between 86% and 94%, and an f1_score of 96%. Random forest classification is a widely adopted Machine Learning approach to construct predictive models across various research domains. However, as models become more complex, their interpretation can be considerably difficult. To interpret the model results, we use both global and local perspectives, incorporating the SHAP (SHapley Additive exPlanations) method. The SHAP technique allows us to analyze individual samples using force plots, and provides a measure of the importance of each geochemical input attribute in the model output.

Ketersediaan
199551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
9 hlm PDF, 2.393 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.23, September 2024
Subjek
Mafic–ultramafic rocks
Geochemical discrimination
Random forest classification
SHAP values
Explainable artificial intelligence
Info Detail Spesifik
-
Pernyataan Tanggungjawab
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Tidak tersedia versi lain

Lampiran Berkas
  • Machine Learning model interpretability using SHAP values: Application to Igneous Rock Classification task
    El Fierro intrusive body is one of the bodies that compose the La Jovita–Las Aguilas mafic–ultramafic belt, located in the Sierra Grande de San Luis, Argentina. The units of this belt carry a base metal sulfide (BMS) mineralization and platinum group minerals (PGM). The macroscopic description of mafic and ultramafic rocks, as is usually done by the mining exploration companies, leads to an imprecise modal classification of the rocks. In this study, we develop a random forest-based prediction model, which uses geochemical parameters to classify mafic and ultramafic rocks intercepted by drill cores. This model showed an accuracy of between 86% and 94%, and an f1_score of 96%. Random forest classification is a widely adopted Machine Learning approach to construct predictive models across various research domains. However, as models become more complex, their interpretation can be considerably difficult. To interpret the model results, we use both global and local perspectives, incorporating the SHAP (SHapley Additive exPlanations) method. The SHAP technique allows us to analyze individual samples using force plots, and provides a measure of the importance of each geochemical input attribute in the model output.
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