{"id":"W2068826480","doi":"10.5539/mas.v2n2p35","title":"Electric Power Customer Positioning Model Based on Decision Tree","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Decision tree; Electric power; Computer science; Construct (python library); ID3 algorithm; Power (physics); ID3; Process (computing); Tree (set theory); Segmentation; Decision tree model; Data mining; Artificial intelligence; Incremental decision tree; Decision tree learning; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001128761,0.0006237306,0.0008166498,0.001414212,0.0005185758,0.001687019,0.001961085,0.0009538756,0.004842178],"category_scores_gemma":[0.002465945,0.0003853126,0.00084106,0.002772619,0.0003549698,0.00193414,0.0005318497,0.0008987717,0.001112837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236517,"about_ca_system_score_gemma":0.0009155523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01450145,"about_ca_topic_score_gemma":0.009127755,"domain_scores_codex":[0.9988262,0.0002996517,0.00008578068,0.0002886301,0.0003272046,0.0001725307],"domain_scores_gemma":[0.9991633,0.0003985443,0.0001003496,0.00003797882,0.0002402096,0.00005966133],"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":[0.0001214055,0.00008092348,0.004764146,0.0000962532,0.00006657436,0.0002701932,0.0001411729,0.8983294,0.0005469036,0.03723305,0.003231023,0.05511891],"study_design_scores_gemma":[0.000008363323,0.00001987592,0.0003943993,0.000005679284,0.00001440408,0.00004208338,0.00001461647,0.9913521,0.0001022935,0.007203457,0.0008333042,0.000009515729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07056111,0.0005734335,0.9107749,0.0009831669,0.0001418047,0.0001368563,0.001367284,0.0007162022,0.01474521],"genre_scores_gemma":[0.9075574,0.0008580947,0.0803208,0.0001125891,0.00009267785,0.0002086053,0.001681886,0.00005169222,0.009116139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01450145,"threshold_uncertainty_score":0.0288341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618573663611076,"score_gpt":0.2460878106299826,"score_spread":0.2299020739938718,"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."}}