File size: 1,763 Bytes
e7b7dd3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
{
"model_name": "GradientBoosting_ActiveLearning",
"approach": "GradientBoosting + Active Learning Simulation",
"base_models": "LogReg + 3x GradientBoosting",
"n_labels": 22,
"labels": [
"Administra\u00e7\u00e3o Geral, Finan\u00e7as e Recursos Humanos",
"Ambiente",
"Atividades Econ\u00f3micas",
"A\u00e7\u00e3o Social",
"Ci\u00eancia",
"Comunica\u00e7\u00e3o e Rela\u00e7\u00f5es P\u00fablicas",
"Coopera\u00e7\u00e3o Externa e Rela\u00e7\u00f5es Internacionais",
"Cultura",
"Desporto",
"Educa\u00e7\u00e3o e Forma\u00e7\u00e3o Profissional",
"Energia e Telecomunica\u00e7\u00f5es",
"Habita\u00e7\u00e3o",
"Obras Particulares",
"Obras P\u00fablicas",
"Ordenamento do Territ\u00f3rio",
"Outros",
"Patrim\u00f3nio",
"Pol\u00edcia Municipal",
"Prote\u00e7\u00e3o Animal",
"Prote\u00e7\u00e3o Civil",
"Sa\u00fade",
"Tr\u00e2nsito, Transportes e Comunica\u00e7\u00f5es"
],
"feature_dimensions": 10768,
"tfidf_features": 10000,
"bert_features": 768,
"active_samples": 100,
"n_gb_models": 3,
"innovations": [
"Multiple GradientBoosting with different hyperparameters",
"Active learning uncertainty sampling simulation",
"Adaptive ensemble weighting by label frequency",
"Dynamic threshold optimization per label",
"Dense feature matrix optimization"
],
"performance": {
"accuracy": 0.45179584120982985,
"f1_macro": 0.5485386636825255,
"f1_micro": 0.7362637362637363,
"hamming_loss": 0.04124420003437017,
"average_precision_macro": 0.6063436422306382
}
} |