{"id":"W2129827556","doi":"10.5539/ijsp.v4n2p1","title":"Model Selection for Poisson Regression via Association Rules Analysis","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Poisson regression; Poisson distribution; Regression analysis; Selection (genetic algorithm); Computer science; Model selection; Regression; Regression diagnostic; Mathematics; Econometrics; Statistics; Machine learning; Polynomial regression; Population","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009890215,0.00005851885,0.0001266754,0.0001105452,0.00004845332,0.0001504035,0.0002796156,0.00003567967,0.000001704858],"category_scores_gemma":[0.0004244189,0.00004785114,0.00004650461,0.0001276747,0.00001328427,0.0003086616,0.00005341584,0.00007282067,6.801428e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001791453,"about_ca_system_score_gemma":0.0001029359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003065876,"about_ca_topic_score_gemma":0.00003019553,"domain_scores_codex":[0.9990925,0.0000306235,0.0003041847,0.0001298988,0.0003678081,0.00007499194],"domain_scores_gemma":[0.9976743,0.0001649914,0.0004179729,0.00008080263,0.001580987,0.00008094939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001623761,0.0006414262,0.07261138,0.00003273772,0.00148783,0.00000286984,0.00182145,0.02180079,0.0005183358,0.3103155,0.01766238,0.572943],"study_design_scores_gemma":[0.0002236934,0.00005981485,0.004775756,0.000005295997,0.00005495256,0.000004970811,0.000004921299,0.7670986,0.00004272231,0.2268904,0.0007949543,0.00004387284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01650557,0.00002268774,0.9817258,0.001235996,0.0001756814,0.00006234781,0.0002306297,0.00000841111,0.00003284793],"genre_scores_gemma":[0.2117965,0.00001794431,0.7879708,0.00004226667,0.000077157,0.000005285552,0.00003567347,0.000002301251,0.00005198654],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7452978,"threshold_uncertainty_score":0.1951313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03726347752418733,"score_gpt":0.3225111672697547,"score_spread":0.2852476897455674,"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."}}