  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 to predict outcomes in intricate datasets. In this paper, we propose
IGANN Sparse, a novel machine learning model from the family of generalized
additive models, which promotes sparsity through a non-linear feature selection
process during training. This ensures interpretability through improved model
sparsity without sacrificing predictive performance. Moreover, IGANN Sparse
serves as an exploratory tool for information systems researchers to unveil
important non-linear relationships in domains that are characterized by complex
patterns. Our ongoing research is directed at a thorough evaluation of the
IGANN Sparse model, including user studies that allow to assess how well users
of the model can benefit from the reduced number of features. This will allow
for a deeper understanding of the interactions between linear vs. non-linear
modeling, number of selected features, and predictive performance.
