What question did this study set out to answer?

The aim is to develop a comprehensive machine learning pipeline for the classification of exoplanets using the Kepler dataset.

May 26, 20260 citationsOpen Access

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Kepler Exoplanet Search Result

DKDua Kalyar

Key Points

The aim is to develop a comprehensive machine learning pipeline for the classification of exoplanets using the Kepler dataset.
Developed an end-to-end pipeline for feature selection and preprocessing of the Kepler dataset.
Trained multiple models including KNN, Random Forest, SVM, and XGBoost.
Evaluated models using metrics such as accuracy, precision, recall, F1/F2 scores, and ROC-AUC.
Successfully detected potential exoplanets with high accuracy rates.
Utilized SHAP and LIME for explainability of the optimal model, enhancing understanding of feature importance.
Found significant improvements in model performance metrics across various algorithms.

Abstract

An end-to-end machine learning pipeline for exoplanet classification is presented in this project. It performs feature selection, preprocesses and balances the features of the Kepler dataset, and trains several models, such as KNN, Random Forest, SVM, and XGBoost. Metrics like accuracy, precision, recall, F1/F2 scores, and ROC-AUC are used to assess model performance. SHAP and LIME are used to further interpret the optimal model in terms of explainability and feature importance. Potential exoplanets can be accurately detected and interpreted from astronomical data thanks to this pipeline.

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Cite This Study

Dua Kalyar (Sat,) studied this question.

synapsesocial.com/papers/6a1538ebb5d9c58d83e8ca9a https://doi.org/https://doi.org/10.5281/zenodo.20364051

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