{"id":"W2897647221","doi":"10.48550/arxiv.1810.10158","title":"Randomized Gradient Boosting Machine","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"Office of Naval Research; National Science Foundation","keywords":"Boosting (machine learning); Computer science; Machine learning; Artificial intelligence; Gradient boosting; Algorithm; Fraction (chemistry)","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":[0.003045952,0.001235544,0.002937318,0.0008633001,0.0005815404,0.001493806,0.00204355,0.001710448,0.002991199],"category_scores_gemma":[0.009433459,0.0005826714,0.001054597,0.001233787,0.001091763,0.001258058,0.001579352,0.002234282,0.002423631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009770367,"about_ca_system_score_gemma":0.001631755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001796779,"about_ca_topic_score_gemma":0.001470671,"domain_scores_codex":[0.9977967,0.001135334,0.00007529651,0.0003129758,0.0005264845,0.0001533211],"domain_scores_gemma":[0.9980929,0.0007939244,0.0001620886,0.0003826617,0.0004508966,0.0001174037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002373051,0.0001504838,0.001853748,0.000386723,0.0003020205,0.0001557074,0.00008587753,0.5304449,0.003294144,0.1492847,0.03093846,0.282866],"study_design_scores_gemma":[0.00003575916,0.00007216768,0.0002174457,0.00002718959,0.00002540195,0.00006624177,0.000006591877,0.9471548,0.0009401513,0.04233937,0.009098435,0.0000165079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003450442,0.0009058501,0.9913265,0.0003848945,0.0002195573,0.00008073411,0.00009835357,0.0009063405,0.002627323],"genre_scores_gemma":[0.3181989,0.00170596,0.6669463,0.001274563,0.0007788447,0.0005360443,0.001043018,0.0004839264,0.009032309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003045952,"threshold_uncertainty_score":0.01610869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04777259867698978,"score_gpt":0.1936371406089367,"score_spread":0.145864541931947,"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."}}