IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight

arXiv:2403.11363v1 Announce Type: new
Abstract: Feature selection is a critical component in predictive analytics that significantly affects the prediction accuracy and interpretability of models. Intrinsic methods for feature selection are built directly into model learning, providing a fast and attractive option for large amounts of data. Machine learning algorithms, such as penalized regression models (e.g., lasso) are the most common choice when it comes to in-built feature selection. However, they fail to capture non-linear relationships, which ultimately affects their ability …

abstract accuracy algorithms analytics arxiv cs.ai cs.cy cs.lg data feature feature selection insight interpretability intrinsic linear machine machine learning machine learning algorithms non-linear prediction predictive predictive analytics sparsity type