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Introduction of high-dimensional interpretable machine learning models and their applications

Abstract : This dissertation focuses on the introduction of new interpretable machine learning methods in a high-dimensional setting. We developped first the C-mix, a mixture model of censored durations that automatically detects subgroups based on the risk that the event under study occurs early; then the binarsity penalty combining a weighted total variation penalty with a linear constraint per block, that applies on one-hot encoding of continuous features; and finally the binacox model that uses the binarsity penalty within a Cox model to automatically detect cut-points in the continuous features. For each method, theoretical properties are established: algorithm convergence, non-asymptotic oracle inequalities, and comparison studies with state-of-the-art methods are carried out on both simulated and real data. All proposed methods give good results in terms of prediction performances, computing time, as well as interpretability abilities.
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Submitted on : Monday, August 31, 2020 - 3:33:30 PM
Last modification on : Friday, August 5, 2022 - 3:00:08 PM


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  • HAL Id : tel-02396796, version 2


Simon Bussy. Introduction of high-dimensional interpretable machine learning models and their applications. Machine Learning [cs.LG]. Sorbonne Université, 2019. English. ⟨NNT : 2019SORUS488⟩. ⟨tel-02396796v2⟩



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