slise

SLISE - Sparse Linear Subset Explanations

The SLISE algorithm can be used for both robust regression and to explain outcomes from black box models. See slise.slise.regression and slise.slise.explain for referense.

In robust regression we fit regression models that can handle data that contains outliers. SLISE accomplishes this by fitting a model such that the largest possible subset of the data items have an error less than a given value. All items with an error larger than that are considered potential outliers and do not affect the resulting model.

SLISE can also be used to provide local model-agnostic explanations for outcomes from black box models. To do this we replace the ground truth response vector with the predictions from the complex model. Furthermore, we force the model to fit a selected item (making the explanation local). This gives us a local approximation of the complex model with a simpler linear model. In contrast to other methods SLISE creates explanations using real data (not some discretised and randomly sampled data) so we can be sure that all inputs are valid (i.e. in the correct data manifold, and follows the constraints used to generate the data, e.g., the laws of physics).

More in-depth details about the algorithm can be found in the papers:

Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K. Sparse Robust Regression for Explaining Classifiers. Discovery Science (DS 2019). Lecture Notes in Computer Science, vol 11828, Springer. https://doi.org/10.1007/978-3-030-33778-0_27

Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K. Robust regression via error tolerance. Data Mining and Knowledge Discovery (2022). https://doi.org/10.1007/s10618-022-00819-2

Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K. Explaining any black box model using real data. Frontiers in Computer Science 5:1143904 (2023). https://doi.org/10.3389/fcomp.2023.1143904