{"id":"W1504509715","doi":"10.1007/11527770_63","title":"On Understanding and Assessing Feature Selection Bias","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Feature (linguistics); Artificial intelligence; Linguistics; Philosophy","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":[0.02494121,0.0009099178,0.001671302,0.003014081,0.0006465021,0.002979668,0.001988399,0.002266121,0.002577362],"category_scores_gemma":[0.1881319,0.0004390418,0.0008927117,0.003082203,0.002237421,0.007377954,0.002416963,0.001873201,0.0004455052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019774,"about_ca_system_score_gemma":0.001101617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002220949,"about_ca_topic_score_gemma":0.001754863,"domain_scores_codex":[0.9896114,0.005518458,0.0006488983,0.001378919,0.002458751,0.0003836585],"domain_scores_gemma":[0.8269898,0.1544432,0.005155539,0.007649965,0.005261361,0.0005001093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006879781,0.0002117448,0.05079375,0.0005112551,0.0005920141,0.0002494714,0.0006756553,0.04890614,0.007059941,0.1217046,0.008555285,0.7600522],"study_design_scores_gemma":[0.0001356241,0.000259441,0.02576181,0.0002135297,0.0002564913,0.0006167265,0.0002898269,0.3110317,0.008203535,0.6480283,0.0051003,0.0001027638],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0594805,0.003463587,0.9303464,0.00184086,0.0001636578,0.0001175739,0.0002240526,0.0005452337,0.003818063],"genre_scores_gemma":[0.6338278,0.001870152,0.3585978,0.001309143,0.0005547443,0.000207771,0.0006387663,0.0002900089,0.002703767],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02494121,"threshold_uncertainty_score":0.1319033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05435074882158655,"score_gpt":0.2900000794928637,"score_spread":0.2356493306712772,"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."}}