{"id":"W4377195153","doi":"10.1007/s00180-023-01364-2","title":"Tree-based boosting with functional data","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Estimator; Computer science; Decision tree; Nonparametric statistics; Identifiability; Tree (set theory); Regression; Mathematics; Machine learning; Econometrics; Mathematical optimization; Algorithm; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004449951,0.0001460977,0.0001835795,0.00008704562,0.0001868819,0.00006078783,0.0002361218,0.00003668755,0.0003865793],"category_scores_gemma":[0.00289247,0.0001231905,0.00001397224,0.0003908229,0.0001203679,0.00005791033,0.0001076964,0.0001345186,0.000156916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002351887,"about_ca_system_score_gemma":0.0002189743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007695219,"about_ca_topic_score_gemma":0.00001308618,"domain_scores_codex":[0.9984995,0.00008715981,0.0002950952,0.0003334752,0.000543913,0.0002408464],"domain_scores_gemma":[0.9904993,0.008716098,0.0001160032,0.0003036848,0.0002653,0.00009961305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004440983,0.00006294308,0.001319487,0.00008326933,0.00003647169,0.00004932078,0.00002806909,0.005974422,0.000006490022,0.8867223,0.07167374,0.03399907],"study_design_scores_gemma":[0.0003120739,0.00005206712,0.02231833,0.0000247044,0.00002356668,0.000003749663,0.00001935537,0.4812744,0.000002124777,0.4954397,0.0004144281,0.0001154907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001231227,0.000005329339,0.9942855,0.0002178463,0.0001317818,0.0001268482,0.00220739,0.0002036462,0.001590411],"genre_scores_gemma":[0.03250514,7.255065e-7,0.9648412,0.000186585,0.0001049604,0.00001306334,0.002148585,0.00002886142,0.0001709062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4753,"threshold_uncertainty_score":0.5023562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3536075023854524,"score_gpt":0.4080748848599703,"score_spread":0.05446738247451793,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}