{"id":"W3170014045","doi":"10.3390/electronics10222862","title":"Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Random forest; Computer science; Scalability; Visual analytics; Visualization; Machine learning; Artificial intelligence; Similarity (geometry); Popularity; Representation (politics); GRASP; Data mining; Feature (linguistics); Human–computer interaction; Data science; Database; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001371708,0.001642757,0.0008463585,0.003589534,0.0005336145,0.002897584,0.001861874,0.00125378,0.01461331],"category_scores_gemma":[0.007929984,0.0005992268,0.001282302,0.001860778,0.0005329931,0.003855418,0.003989399,0.001659202,0.003359315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005275626,"about_ca_system_score_gemma":0.0009814005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003588269,"about_ca_topic_score_gemma":0.004177224,"domain_scores_codex":[0.9994122,0.0001524882,0.00003688002,0.0001024881,0.000227215,0.00006868995],"domain_scores_gemma":[0.9981825,0.0008187245,0.0001432928,0.0002859572,0.000424097,0.000145507],"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":[0.001089219,0.0002795776,0.003688106,0.001306006,0.0002337099,0.000873115,0.001834506,0.09973433,0.02619706,0.06210484,0.09488062,0.7077789],"study_design_scores_gemma":[0.0001098192,0.0001080127,0.001492291,0.0002148083,0.00006151258,0.0004986115,0.0003828786,0.8155438,0.01818189,0.1030187,0.06025338,0.0001342759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00653378,0.0004086899,0.9666203,0.0004016594,0.0000852131,0.000154161,0.002543987,0.02095603,0.002296255],"genre_scores_gemma":[0.1928326,0.0009694809,0.7923303,0.0003279895,0.0001285689,0.0006706099,0.005704459,0.00416392,0.002872164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01461331,"threshold_uncertainty_score":0.04888636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255610545355446,"score_gpt":0.3170688413211087,"score_spread":0.3045127358675542,"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."}}