{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:58:14Z","timestamp":1742943494436,"version":"3.40.3"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031264214"},{"type":"electronic","value":"9783031264221"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T00:00:00Z","timestamp":1679097600000},"content-version":"vor","delay-in-days":76,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We introduce a Python library, called <jats:sc>slisemap<\/jats:sc>, that contains a supervised dimensionality reduction method that can be used for global explanation of <jats:italic>black box<\/jats:italic> regression or classification models. <jats:sc>slisemap<\/jats:sc> takes a data matrix and predictions from a <jats:italic>black box<\/jats:italic> model as input, and outputs a (typically) two-dimensional embedding, such that the black box model can be approximated, to a good fidelity, by the same interpretable <jats:italic>white box<\/jats:italic> model for points with similar embeddings. The library includes basic visualisation tools and extensive documentation, making it easy to get started and obtain useful insights. The <jats:sc>slisemap<\/jats:sc> library is published on GitHub and PyPI under an open source license.\n<\/jats:p>","DOI":"10.1007\/978-3-031-26422-1_41","type":"book-chapter","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:08:37Z","timestamp":1679879317000},"page":"612-616","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SLISEMAP: Combining Supervised Dimensionality Reduction with Local Explanations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7749-2918","authenticated-orcid":false,"given":"Anton","family":"Bj\u00f6rklund","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8788-3939","authenticated-orcid":false,"given":"Jarmo","family":"M\u00e4kel\u00e4","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1819-1047","authenticated-orcid":false,"given":"Kai","family":"Puolam\u00e4ki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"41_CR1","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rklund, A., Henelius, A., Oikarinen, E., Kallonen, K., Puolam\u00e4ki, K.: Sparse robust regression for explaining classifiers. In: Discovery Science, vol. 11828, pp. 351\u2013366 (2019)","DOI":"10.1007\/978-3-030-33778-0_27"},{"key":"41_CR2","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rklund, A., Henelius, A., Oikarinen, E., Kallonen, K., Puolam\u00e4ki, K.: Robust regression via error tolerance. Data Min. Knowl. Discov. 36, 781\u2013810 (2022)","DOI":"10.1007\/s10618-022-00819-2"},{"key":"41_CR3","doi-asserted-by":"publisher","unstructured":"Bj\u00f6rklund, A., M\u00e4kel\u00e4, J., Puolam\u00e4ki, K.: SLISEMAP: Supervised dimensionality reduction through local explanations. Mach. 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