{"id":"W4385486392","doi":"10.1007/978-3-031-33390-3_11","title":"Boosting","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Boosting (machine learning); Gradient boosting; Multinomial logistic regression; Artificial intelligence; Computer science; Machine learning; AdaBoost; Random forest; Mathematics; Classifier (UML)","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005451122,0.0006858439,0.0007548344,0.0002555424,0.0008120132,0.001009828,0.0006006699,0.0002969482,0.000007199894],"category_scores_gemma":[0.00008832846,0.0007338351,0.00007224514,0.0001222794,0.0002241397,0.0001077514,0.001546764,0.0009084301,0.00003699692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002895926,"about_ca_system_score_gemma":0.0001506191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002842381,"about_ca_topic_score_gemma":0.000007574866,"domain_scores_codex":[0.9966611,0.0000500097,0.0008822267,0.001205294,0.0004975571,0.000703853],"domain_scores_gemma":[0.9971252,0.001167829,0.0006066288,0.0005351715,0.0002259632,0.0003391738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001793378,0.00000660789,0.00002038056,0.0001307006,0.00005597986,0.00009681535,0.0004530204,0.0001697375,0.000003175478,0.7006793,0.0009598143,0.2974227],"study_design_scores_gemma":[0.0002326849,0.000141325,0.0001383767,0.0005034335,0.0000548468,0.00009705179,0.00003246329,0.6431707,0.000002175844,0.3524867,0.002428323,0.0007119161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001170605,0.0007091512,0.9632402,0.0002062574,0.0007911596,0.000205294,0.0001308378,0.0004571654,0.03414287],"genre_scores_gemma":[0.08974124,0.001619088,0.8383316,0.0009117764,0.0007908524,0.000003119191,0.0001557028,0.0002946731,0.068152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.643001,"threshold_uncertainty_score":0.9995112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03486239759677846,"score_gpt":0.2658821743200783,"score_spread":0.2310197767232999,"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."}}