{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001959326,0.001054205,0.001898971,0.00102845,0.0006163148,0.001161467,0.002258507,0.001130099,0.002391738],"category_scores_gemma":[0.002570523,0.0005127857,0.001117521,0.001024721,0.0005601002,0.001265998,0.0006513648,0.001135758,0.0009633918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008753439,"about_ca_system_score_gemma":0.00149937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01586937,"about_ca_topic_score_gemma":0.007602681,"domain_scores_codex":[0.9989122,0.000259561,0.00005483179,0.0003057846,0.0002958107,0.0001719353],"domain_scores_gemma":[0.9991698,0.0002928402,0.0000797706,0.00004666056,0.000367981,0.00004295335],"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":[0.0001238754,0.00005202935,0.002800964,0.00008684862,0.00008242793,0.0001478072,0.00004604611,0.9252752,0.001290883,0.008483051,0.003181906,0.05842892],"study_design_scores_gemma":[0.00000587827,0.00001354969,0.0001759028,0.000004299183,0.000009529561,0.00002070201,0.000002029766,0.9975365,0.0001415044,0.001723712,0.0003604686,0.000005906547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02576041,0.001171333,0.9671143,0.0004998035,0.0002009834,0.0001097635,0.0003026637,0.0009455839,0.003895183],"genre_scores_gemma":[0.8688113,0.001379344,0.1186633,0.0003120092,0.0002707156,0.0003904841,0.0009186136,0.0001408286,0.009113329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01586937,"threshold_uncertainty_score":0.03155404,"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."}}