{"id":"W3137686095","doi":"10.1109/bigdata50022.2020.9378000","title":"Increasing Prediction Accuracy for Human Activity Recognition Using Optimized Hyperparameters","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Hyperparameter; Random forest; Support vector machine; Computer science; Naive Bayes classifier; Machine learning; Artificial intelligence; Activity recognition; Hyperparameter optimization; Context (archaeology); Decision tree","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":[0.003065315,0.00154078,0.0008664816,0.0009287768,0.0003617273,0.0008808463,0.0006325446,0.001317414,0.0009666184],"category_scores_gemma":[0.0113852,0.0004275968,0.0007841842,0.0005680277,0.0003113319,0.001395896,0.0004413465,0.001449572,0.0006275719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008113199,"about_ca_system_score_gemma":0.0009200344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01454402,"about_ca_topic_score_gemma":0.008636907,"domain_scores_codex":[0.9985655,0.0004424659,0.0001030714,0.0004741893,0.000224273,0.0001905316],"domain_scores_gemma":[0.9960491,0.002601855,0.0002213024,0.0003329698,0.000720923,0.00007371195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005187301,0.0002536802,0.01921836,0.00008079324,0.0001780066,0.0001079856,0.00009339845,0.7576351,0.006514327,0.0004661646,0.003423005,0.2115105],"study_design_scores_gemma":[0.00001840968,0.00006626887,0.003788644,0.00001974874,0.00002554244,0.00003792031,0.00002599396,0.9914117,0.003681983,0.0006010942,0.0003047001,0.00001795424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5479459,0.003124943,0.4351494,0.000988643,0.0003271915,0.0001598166,0.0007623113,0.006427164,0.005114703],"genre_scores_gemma":[0.9511534,0.0001798926,0.04692974,0.0001119155,0.00003134026,0.00006063463,0.0006494066,0.0001030749,0.0007806213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01454402,"threshold_uncertainty_score":0.02891868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1726874806045976,"score_gpt":0.3198375199123713,"score_spread":0.1471500393077737,"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."}}