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abstract as well maybe
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chhoumann committed Jun 12, 2024
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\begin{abstract}
Abstract
This thesis advances the analysis of \gls{libs} data for predicting major oxide compositions in geological samples.
By integrating machine learning techniques and ensemble regression models, the study addresses challenges like high dimensionality, non-linearity, multicollinearity, and limited data availability.
Key innovations include the use of stacked generalization for improved model performance and automated hyperparameter optimization with Optuna.
The research contributes a comprehensive catalog of models and preprocessing techniques, and integrates findings into the \gls{pyhat} by the \gls{usgs}, enhancing its scientific capabilities.
This work lays a robust foundation for future advancements in geochemical analysis and planetary exploration using \gls{libs} data.
\end{abstract}

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