{"id":"W4315777889","doi":"10.1109/icnsc55942.2022.10004112","title":"Model of Gradient Boosting Random Forest Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Xidian University; Science and Technology Development Fund; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Random forest; Gradient boosting; Interpretability; Boosting (machine learning); Decision tree; Computer science; Artificial intelligence; Random tree; Machine learning; Data mining; Statistical classification; Pattern recognition (psychology); Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004005013,0.0001413121,0.0001938957,0.0001532848,0.0004119531,0.0001011951,0.0003672461,0.00003867902,0.00001673358],"category_scores_gemma":[0.00001353872,0.0001486206,0.0000891582,0.0001731234,0.00005272209,0.0001121274,0.0001281759,0.0002790864,0.000001201576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007926807,"about_ca_system_score_gemma":0.00005404149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008408758,"about_ca_topic_score_gemma":0.00002671136,"domain_scores_codex":[0.998575,0.00008642857,0.0003515992,0.0003812896,0.0004137657,0.0001919088],"domain_scores_gemma":[0.9991488,0.0001020729,0.0002877729,0.0002537978,0.0001463277,0.00006116855],"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":[0.0005266901,0.0002662917,0.001483069,0.00001612913,0.0002455798,0.00001698868,0.001058565,0.3683449,0.02635599,0.3509818,0.003082441,0.2476215],"study_design_scores_gemma":[0.0008575147,0.0001477602,0.000126151,0.0000278057,0.00001364154,0.00003009402,0.00005424824,0.9844559,0.0002685778,0.01187162,0.002021948,0.0001247283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02853809,0.00005386107,0.9630236,0.001042493,0.0009163987,0.0002626959,0.00003659891,0.0001838261,0.00594245],"genre_scores_gemma":[0.9968742,0.00007096597,0.001854377,0.0005481223,0.0001956073,0.00005024684,0.00001135838,0.00001063179,0.0003844804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9683361,"threshold_uncertainty_score":0.6060571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04384461435494658,"score_gpt":0.2552293678178009,"score_spread":0.2113847534628543,"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."}}