{"id":"W4379518180","doi":"10.21428/594757db.c4a784aa","title":"Machine Learning for Hotel Booking Cancellations Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","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":[0.001082026,0.0007624297,0.001290516,0.001242506,0.0004832022,0.0008226877,0.001372373,0.001069086,0.00322416],"category_scores_gemma":[0.002564795,0.0003425378,0.001015548,0.001190403,0.0002169768,0.0006428194,0.0005257003,0.001899813,0.001519751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008024119,"about_ca_system_score_gemma":0.001476939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02915993,"about_ca_topic_score_gemma":0.02108035,"domain_scores_codex":[0.9994401,0.0001211241,0.0000461877,0.0001721138,0.00008708279,0.0001332975],"domain_scores_gemma":[0.9982863,0.001008673,0.0001233802,0.0001278128,0.0003546526,0.00009916071],"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":[0.0005958678,0.001630905,0.0286535,0.0001503161,0.000209057,0.0001330949,0.00003267352,0.4629906,0.001529132,0.001205907,0.02105816,0.4818108],"study_design_scores_gemma":[0.000006265788,0.00002158137,0.001278224,0.000004433298,0.000008950676,0.000007554294,0.00000575077,0.9977449,0.0002332408,0.0004226623,0.0002623796,0.000003986098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6514893,0.007595645,0.315681,0.002540459,0.001103811,0.0002083509,0.008165133,0.004957758,0.00825851],"genre_scores_gemma":[0.9586733,0.0005511561,0.02751515,0.0001589949,0.0003380317,0.00007176364,0.005735775,0.00004887069,0.006906885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02915993,"threshold_uncertainty_score":0.05798042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291868354251896,"score_gpt":0.2048800545301138,"score_spread":0.1719613709875949,"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."}}