{"id":"W2604652933","doi":"10.3934/bdia.2016012","title":"Modeling daily guest count prediction","year":2017,"lang":"en","type":"article","venue":"Big Data and Information Analytics","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Lasso (programming language); Computer science; Data mining; Preprocessor; Data pre-processing; Count data; Transaction data; Feature (linguistics); Data modeling; Regression; Artificial intelligence; Predictive modelling; Machine learning; Regression analysis; Poisson distribution; Database transaction; Statistics; Mathematics; Database","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":[0.00124649,0.0007940983,0.0009611025,0.0009610453,0.0003301247,0.0009897839,0.00144603,0.0007946407,0.001546475],"category_scores_gemma":[0.003432002,0.0004784719,0.0009374512,0.001452288,0.0003280281,0.0010998,0.0006386778,0.001254356,0.0005182328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005728462,"about_ca_system_score_gemma":0.0008136407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01240019,"about_ca_topic_score_gemma":0.01245339,"domain_scores_codex":[0.999426,0.000137622,0.00002830626,0.0002036112,0.0001264734,0.00007789209],"domain_scores_gemma":[0.9987528,0.0006571541,0.0002323419,0.0001177248,0.0001826466,0.00005735419],"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":[0.0001149643,0.0001180086,0.01474305,0.00007617179,0.0001301996,0.0001540898,0.00008703473,0.9041814,0.001857943,0.00869085,0.004044923,0.06580139],"study_design_scores_gemma":[0.000001015397,0.000005446134,0.0003752631,0.000001508263,0.000002899186,0.000006935326,0.000003651824,0.9983753,0.00009377005,0.0008894798,0.0002422148,0.000002391766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08831459,0.0005118177,0.9062373,0.0005270301,0.0001221382,0.00004101778,0.001281037,0.0009547944,0.00201031],"genre_scores_gemma":[0.8439324,0.0006265417,0.1440393,0.000176964,0.0003568086,0.0001833867,0.004265863,0.0001686837,0.00625003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01240019,"threshold_uncertainty_score":0.024656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018346827705457,"score_gpt":0.2750029553143635,"score_spread":0.1731682725438178,"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."}}