{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000188911,0.00008289968,0.0001245074,0.00002420485,0.00009990392,0.000244852,0.000153103,0.00004896596,0.000004127708],"category_scores_gemma":[0.0001740602,0.00008679296,0.00004464407,0.0003489074,0.00002954384,0.000406253,0.0001055065,0.00005912571,0.000003649839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008796259,"about_ca_system_score_gemma":0.0002009706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000391276,"about_ca_topic_score_gemma":0.0002466617,"domain_scores_codex":[0.9990932,0.00005895888,0.0001749577,0.0003022997,0.000151919,0.0002186522],"domain_scores_gemma":[0.9994494,0.00008172415,0.00005874853,0.0001874395,0.0001638678,0.00005880686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000192106,0.0002901079,0.004452222,0.00009774857,0.0001081145,0.00001424848,0.0004328386,0.003927201,0.0003800422,0.7322636,0.008861985,0.2489798],"study_design_scores_gemma":[0.001254667,0.00009882358,0.0002904914,0.00001127461,0.00001799871,0.00001727901,0.00004857606,0.948373,0.002053878,0.03058771,0.01713584,0.000110445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003160576,0.001028667,0.9945468,0.0006864699,0.00009387465,0.0001278607,0.00001184962,0.00006181541,0.0002820332],"genre_scores_gemma":[0.9731113,0.0005122558,0.02405492,0.001266783,0.00007860208,0.0000272107,0.0004931157,0.0000118001,0.0004440441],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9704919,"threshold_uncertainty_score":0.3539314,"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."}}