{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001086591,0.000838288,0.001247651,0.001110525,0.0008043572,0.001807769,0.00154063,0.001278757,0.05579492],"category_scores_gemma":[0.003963874,0.0004694403,0.0008577213,0.001236528,0.0005912289,0.001267054,0.001293586,0.001964336,0.03423324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005389618,"about_ca_system_score_gemma":0.000819378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001109996,"about_ca_topic_score_gemma":0.00179208,"domain_scores_codex":[0.9991292,0.0002127761,0.00002158562,0.0002519278,0.0002876039,0.00009689377],"domain_scores_gemma":[0.9990613,0.0002652357,0.0000342074,0.0003393987,0.0002270807,0.00007273213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001178642,0.000104113,0.0003537794,0.000183249,0.00007872094,0.000035883,0.00004023829,0.02487696,0.001981569,0.1485119,0.1903915,0.6333242],"study_design_scores_gemma":[0.00007667602,0.00009659206,0.0006537407,0.000123807,0.00008254081,0.0002951379,0.00003036374,0.2385848,0.00454663,0.3366602,0.4188155,0.0000341019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004771165,0.004591346,0.7952481,0.001473193,0.001932502,0.0002357822,0.001427895,0.007425141,0.1828948],"genre_scores_gemma":[0.1423851,0.003606331,0.5642023,0.003004943,0.002217228,0.0005091664,0.00480698,0.003572033,0.2756958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05579492,"threshold_uncertainty_score":0,"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."}}