{"id":"W2788320705","doi":"","title":"Machine learning: Supervised methods, SVM and kNN","year":2018,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Support vector machine; Machine learning; Artificial intelligence; Random forest; Logistic regression; Supervised learning; Context (archaeology); Computer science; Pattern recognition (psychology); Set (abstract data type); Artificial neural network; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01922479,0.0006989539,0.0007138298,0.0003159737,0.001202687,0.001795942,0.002934507,0.0005710683,0.0005078304],"category_scores_gemma":[0.006993035,0.0007541268,0.0002527955,0.0008047794,0.0008324938,0.0005634659,0.004485357,0.001908143,0.0003366269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001371691,"about_ca_system_score_gemma":0.0004194688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004810594,"about_ca_topic_score_gemma":0.0009416902,"domain_scores_codex":[0.9673991,0.02826134,0.000945891,0.002046046,0.000634674,0.000712917],"domain_scores_gemma":[0.9877689,0.003986633,0.0009526029,0.003959116,0.002805935,0.0005268183],"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":[0.00001500533,0.0004284972,0.00828987,0.0002419785,0.00009798796,0.000005829642,0.01153792,0.00004628718,0.002068009,0.47826,0.0009798605,0.4980287],"study_design_scores_gemma":[0.0006100874,0.000002092137,0.01524258,0.001038124,0.00007373538,0.00005249515,0.00006431302,0.5827935,0.00490495,0.007120211,0.3874019,0.0006960193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006239628,0.005582861,0.8813257,0.04649284,0.0006583141,0.0004907396,0.00005976373,0.0005354835,0.05861469],"genre_scores_gemma":[0.1245199,0.004937148,0.7945145,0.0002841108,0.0001275472,0.00008168464,0.00141718,0.0001021681,0.07401568],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5827472,"threshold_uncertainty_score":0.999491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03162811474333381,"score_gpt":0.2822962049146925,"score_spread":0.2506680901713587,"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."}}