{"id":"https://openalex.org/W4400406263","doi":"https://doi.org/10.1007/s10994-024-06590-3","title":"Fast linear model trees by PILOT","display_name":"Fast linear model trees by PILOT","publication_year":2024,"publication_date":"2024-07-08","ids":{"openalex":"https://openalex.org/W4400406263","doi":"https://doi.org/10.1007/s10994-024-06590-3"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-024-06590-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-024-06590-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-024-06590-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-024-06590-3.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091795973","display_name":"Jakob Raymaekers","orcid":"https://orcid.org/0000-0002-2093-3137"},"institutions":[{"id":"https://openalex.org/I149213910","display_name":"University of Antwerp","ror":"https://ror.org/008x57b05","country_code":"BE","type":"education","lineage":["https://openalex.org/I149213910"]},{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]},{"id":"https://openalex.org/I4210111370","display_name":"ZNA Middelheim Hospital","ror":"https://ror.org/01z5jvj74","country_code":"BE","type":"healthcare","lineage":["https://openalex.org/I4210111370"]}],"countries":["BE","NL"],"is_corresponding":false,"raw_author_name":"Jakob Raymaekers","raw_affiliation_strings":["Department of Mathematics, University of Antwerp, Middelheimlaan\u00a01, 2020, Antwerp, Belgium","Department of Quantitative Economics, Maastricht University, Maastricht, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Antwerp, Middelheimlaan\u00a01, 2020, Antwerp, Belgium","institution_ids":["https://openalex.org/I4210111370","https://openalex.org/I149213910"]},{"raw_affiliation_string":"Department of Quantitative Economics, Maastricht University, Maastricht, The Netherlands","institution_ids":["https://openalex.org/I34352273"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075121395","display_name":"Peter J. Rousseeuw","orcid":"https://orcid.org/0000-0002-3807-5353"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Peter J. Rousseeuw","raw_affiliation_strings":["Section of Statistics and Data Science, KU Leuven, Celestijnenlaan\u00a0200B, 3001, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"Section of Statistics and Data Science, KU Leuven, Celestijnenlaan\u00a0200B, 3001, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085208338","display_name":"Tim Verdonck","orcid":"https://orcid.org/0000-0003-1105-2028"},"institutions":[{"id":"https://openalex.org/I149213910","display_name":"University of Antwerp","ror":"https://ror.org/008x57b05","country_code":"BE","type":"education","lineage":["https://openalex.org/I149213910"]},{"id":"https://openalex.org/I4210111370","display_name":"ZNA Middelheim Hospital","ror":"https://ror.org/01z5jvj74","country_code":"BE","type":"healthcare","lineage":["https://openalex.org/I4210111370"]},{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Tim Verdonck","raw_affiliation_strings":["Department of Mathematics, University of Antwerp, Middelheimlaan\u00a01, 2020, Antwerp, Belgium","Section of Statistics and Data Science, KU Leuven, Celestijnenlaan\u00a0200B, 3001, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Antwerp, Middelheimlaan\u00a01, 2020, Antwerp, Belgium","institution_ids":["https://openalex.org/I4210111370","https://openalex.org/I149213910"]},{"raw_affiliation_string":"Section of Statistics and Data Science, KU Leuven, Celestijnenlaan\u00a0200B, 3001, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082422845","display_name":"Ruicong Yao","orcid":"https://orcid.org/0000-0001-7581-1797"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Ruicong Yao","raw_affiliation_strings":["Section of Statistics and Data Science, KU Leuven, Celestijnenlaan\u00a0200B, 3001, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"Section of Statistics and Data Science, KU Leuven, Celestijnenlaan\u00a0200B, 3001, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075121395"],"corresponding_institution_ids":["https://openalex.org/I99464096"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":3.7474,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.93914635,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"113","issue":"9","first_page":"6561","last_page":"6610"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9537000060081482,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.652930498123169},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6474863290786743},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.6019806861877441},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5528992414474487},{"id":"https://openalex.org/keywords/piecewise-linear-function","display_name":"Piecewise linear function","score":0.5268539190292358},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5234403014183044},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5168024301528931},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5032572150230408},{"id":"https://openalex.org/keywords/alternating-decision-tree","display_name":"Alternating decision tree","score":0.4849169850349426},{"id":"https://openalex.org/keywords/proper-linear-model","display_name":"Proper linear model","score":0.46008753776550293},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4429435729980469},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4420904815196991},{"id":"https://openalex.org/keywords/segmented-regression","display_name":"Segmented regression","score":0.43390369415283203},{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.4223261773586273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41159120202064514},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.4087026119232178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39028680324554443},{"id":"https://openalex.org/keywords/decision-tree-learning","display_name":"Decision tree learning","score":0.2528803050518036},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18961796164512634},{"id":"https://openalex.org/keywords/incremental-decision-tree","display_name":"Incremental decision tree","score":0.15116235613822937},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10064402222633362}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.652930498123169},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6474863290786743},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.6019806861877441},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5528992414474487},{"id":"https://openalex.org/C17095337","wikidata":"https://www.wikidata.org/wiki/Q2375229","display_name":"Piecewise linear function","level":2,"score":0.5268539190292358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5234403014183044},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5168024301528931},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5032572150230408},{"id":"https://openalex.org/C120136583","wikidata":"https://www.wikidata.org/wiki/Q4736414","display_name":"Alternating decision tree","level":5,"score":0.4849169850349426},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.46008753776550293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4429435729980469},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4420904815196991},{"id":"https://openalex.org/C35519122","wikidata":"https://www.wikidata.org/wiki/Q3775699","display_name":"Segmented regression","level":4,"score":0.43390369415283203},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.4223261773586273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41159120202064514},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.4087026119232178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39028680324554443},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.2528803050518036},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18961796164512634},{"id":"https://openalex.org/C10229987","wikidata":"https://www.wikidata.org/wiki/Q17083028","display_name":"Incremental decision tree","level":4,"score":0.15116235613822937},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10064402222633362},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10994-024-06590-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-024-06590-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-024-06590-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:cris.maastrichtuniversity.nl:openaire/222a7830-8e9b-453a-9d3d-5921c894c985","is_oa":true,"landing_page_url":"https://cris.maastrichtuniversity.nl/en/publications/222a7830-8e9b-453a-9d3d-5921c894c985","pdf_url":null,"source":{"id":"https://openalex.org/S4306402616","display_name":"Research Publications (Maastricht University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I34352273","host_organization_name":"Maastricht University","host_organization_lineage":["https://openalex.org/I34352273"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Raymaekers, J, Rousseeuw, P J, Verdonck, T & Yao, R 2024, 'Fast linear model trees by PILOT', Machine Learning, vol. 113, no. 9, pp. 6561-6610. https://doi.org/10.1007/s10994-024-06590-3","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:lirias2repo.kuleuven.be:20.500.12942/754366","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/20.500.12942/754366","pdf_url":"https://lirias.kuleuven.be/retrieve/563bd427-88af-41ff-8e65-58f8f44c453c","source":{"id":"https://openalex.org/S4306401954","display_name":"Lirias (KU Leuven)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99464096","host_organization_name":"KU Leuven","host_organization_lineage":["https://openalex.org/I99464096"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning, vol. 113 (9), (6561-6610)","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/s10994-024-06590-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-024-06590-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-024-06590-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1136205261","display_name":null,"funder_award_id":"C16/15/068","funder_id":"https://openalex.org/F4320322308","funder_display_name":"KU Leuven"},{"id":"https://openalex.org/G1533460378","display_name":null,"funder_award_id":"C16/15/068","funder_id":"https://openalex.org/F4320322681","funder_display_name":"Onderzoeksraad, KU Leuven"},{"id":"https://openalex.org/G5085058989","display_name":null,"funder_award_id":"10301","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G5404998627","display_name":null,"funder_award_id":"Chair","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G6477286732","display_name":"Robust Causal Discovery","funder_award_id":"101103017","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G6531840109","display_name":null,"funder_award_id":"Robust Predictive Analytics","funder_id":"https://openalex.org/F4320307790","funder_display_name":"BASF"},{"id":"https://openalex.org/G7895516993","display_name":null,"funder_award_id":"Belgium","funder_id":"https://openalex.org/F4320322308","funder_display_name":"KU Leuven"},{"id":"https://openalex.org/G8051717526","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8318064016","display_name":null,"funder_award_id":"Horizon","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8630528832","display_name":null,"funder_award_id":"01103017","funder_id":"https://openalex.org/F4320338448","funder_display_name":"HORIZON EUROPE European Innovation Council"}],"funders":[{"id":"https://openalex.org/F4320307790","display_name":"BASF","ror":"https://ror.org/01q8f6705"},{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320322308","display_name":"KU Leuven","ror":"https://ror.org/05f950310"},{"id":"https://openalex.org/F4320322681","display_name":"Onderzoeksraad, KU Leuven","ror":"https://ror.org/05f950310"},{"id":"https://openalex.org/F4320327336","display_name":"Vlaamse regering","ror":null},{"id":"https://openalex.org/F4320338448","display_name":"HORIZON EUROPE European Innovation Council","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4400406263.pdf"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W168150807","https://openalex.org/W182248535","https://openalex.org/W1484867920","https://openalex.org/W1741702827","https://openalex.org/W1961953963","https://openalex.org/W1997878053","https://openalex.org/W2033371754","https://openalex.org/W2060270632","https://openalex.org/W2066442872","https://openalex.org/W2073939915","https://openalex.org/W2082137964","https://openalex.org/W2107170260","https://openalex.org/W2121044470","https://openalex.org/W2135046866","https://openalex.org/W2136478625","https://openalex.org/W2136944412","https://openalex.org/W2162387923","https://openalex.org/W2170837895","https://openalex.org/W2295598076","https://openalex.org/W2329290504","https://openalex.org/W2330820318","https://openalex.org/W2552660223","https://openalex.org/W2586821431","https://openalex.org/W2768348081","https://openalex.org/W2787894218","https://openalex.org/W2888487581","https://openalex.org/W2906432502","https://openalex.org/W2911964244","https://openalex.org/W2952563653","https://openalex.org/W2963434390","https://openalex.org/W2968325077","https://openalex.org/W2995150843","https://openalex.org/W3004343334","https://openalex.org/W3046234331","https://openalex.org/W3097950615","https://openalex.org/W3098635105","https://openalex.org/W3104060823","https://openalex.org/W4206039841","https://openalex.org/W4214905925","https://openalex.org/W4256170606","https://openalex.org/W4286493286","https://openalex.org/W4308326596","https://openalex.org/W6675321329","https://openalex.org/W6675354045","https://openalex.org/W6843735874"],"related_works":["https://openalex.org/W1685088304","https://openalex.org/W623416330","https://openalex.org/W2743208879","https://openalex.org/W600390644","https://openalex.org/W4249387689","https://openalex.org/W4238233914","https://openalex.org/W2066533688","https://openalex.org/W267133670","https://openalex.org/W1523442101","https://openalex.org/W3133886840"],"abstract_inverted_index":{"Abstract":[0],"Linear":[1,132],"model":[2,49,88,116,176,192],"trees":[3,6,22,50,89,172,177],"are":[4,51,65],"regression":[5,75,105],"that":[7,90,141,165],"incorporate":[8],"linear":[9,33,48,87,121,175,204],"models":[10,122],"in":[11,99,123,189],"the":[12,17,25,124,139,149,198,206],"leaf":[13],"nodes.":[14,125],"This":[15],"preserves":[16],"intuitive":[18],"interpretation":[19],"of":[20,181],"decision":[21,40,171],"and":[23,54,70,95,114,153,173],"at":[24],"same":[26,150],"time":[27,52,152],"enables":[28],"them":[29],"to":[30,58,68,138,168],"better":[31],"capture":[32],"relationships,":[34],"which":[35],"is":[36,91,144,200,209],"hard":[37],"for":[38,46,86,119,130],"standard":[39,74,170],"trees.":[41,76],"But":[42],"most":[43],"existing":[44],"methods":[45],"fitting":[47,120],"consuming":[53],"therefore":[55],"not":[56],"scalable":[57],"large":[59],"data":[60,182,199],"sets.":[61,183],"In":[62,77],"addition,":[63],"they":[64],"more":[66],"prone":[67],"overfitting":[69],"extrapolation":[71],"issues":[72],"than":[73],"this":[78],"paper":[79],"we":[80,185],"introduce":[81],"PILOT,":[82],"a":[83,100,115,179,203],"new":[84],"algorithm":[85],"fast,":[92],"regularized,":[93],"stable":[94],"interpretable.":[96],"PILOT":[97,128,147,166],"trains":[98],"greedy":[101],"fashion":[102],"like":[103],"classic":[104],"trees,":[106],"but":[107],"incorporates":[108],"an":[109,190],"L":[110],"2":[111],"boosting":[112],"approach":[113],"selection":[117],"rule":[118],"The":[126],"abbreviation":[127],"stands":[129],"PIecewise":[131],"Organic":[133],"Tree,":[134],"where":[135],"\u2018organic\u2019":[136],"refers":[137],"fact":[140],"no":[142],"pruning":[143],"carried":[145],"out.":[146],"has":[148],"low":[151],"space":[154],"complexity":[155],"as":[156],"CART":[157],"without":[158],"its":[159,187],"pruning.":[160],"An":[161],"empirical":[162],"study":[163],"indicates":[164],"tends":[167],"outperform":[169],"other":[174],"on":[178],"variety":[180],"Moreover,":[184],"prove":[186],"consistency":[188],"additive":[191],"setting":[193],"under":[194],"weak":[195],"assumptions.":[196],"When":[197],"generated":[201],"by":[202],"model,":[205],"convergence":[207],"rate":[208],"polynomial.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}