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Models/Logistic Regression

Logistic Regression

Reported on 37 benchmarks across 12 tasks · 5 papers · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing12 results

Abuse DetectiononAutomatic Misogynistic Identification
Accuracy· 2018-12-17
0.704
best: 0.832 (mBert)
SOTA
Hateminers : Detecting Hate speech against Women arXiv:1812.06700
Hate Speech DetectiononAutomatic Misogynistic Identification
Accuracy· 2018-12-17
0.704
best: 0.832 (mBert)
SOTA
Hateminers : Detecting Hate speech against Women arXiv:1812.06700
Text ClassificationonOneStopEnglish (Readability Assessment)
Accuracy (5-fold)· 2021-06-15
0.744
best: 0.99 (RoBERTa-RF-T1 hybrid)
BERT Embeddings for Automatic Readability Assessment arXiv:2106.07935
Abuse DetectiononHopeEDI
Weighted Average F1-score
0.56
best: 0.93 (RoBERTa)
Hate Speech DetectiononHopeEDI
Weighted Average F1-score
0.56
best: 0.93 (RoBERTa)
Text ClassificationonIMDb Movie Reviews
AUC
0.84
Text ClassificationonTwitter Sentiment Analysis
AUC
0.9298
Fact VerificationonKILT: FEVER
Accuracy
71.24
best: 89.55 (Re2G)
Fact VerificationonKILT: FEVER
KILT-AC
0
best: 78.53 (Re2G)
Fact VerificationonKILT: FEVER
R-Prec
0
best: 88.92 (Re2G)
Fact VerificationonKILT: FEVER
Recall@5
0
best: 92.52 (Re2G)
Hope Speech DetectiononHopeEDI
Weighted Average F1-score
0.56
best: 0.93 (RoBERTa)

Methodology11 results

AutoMLonWine
accuracy· 2025-05-25
98.33
SOTA
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization arXiv:2505.19205
AutoMLonBreast Cancer Coimbra Data Set
Accuracy· 2025-05-25
97.02
SOTA
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization arXiv:2505.19205
ClassificationonOneStopEnglish (Readability Assessment)
Accuracy (5-fold)· 2021-06-15
0.744
best: 0.99 (RoBERTa-RF-T1 hybrid)
BERT Embeddings for Automatic Readability Assessment arXiv:2106.07935
Multi-Label ClassificationonMIMIC-III
Macro-AUC· 2018-02-15
56.1
best: 96.2 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Multi-Label ClassificationonMIMIC-III
Macro-F1· 2018-02-15
1.1
best: 24.7 (PLM-CA)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Multi-Label ClassificationonMIMIC-III
Micro-AUC· 2018-02-15
93.7
best: 99.3 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Multi-Label ClassificationonMIMIC-III
Micro-F1· 2018-02-15
27.2
best: 61.2 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Multi-Label ClassificationonMIMIC-III
Precision@15· 2018-02-15
41.1
best: 62.4 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Multi-Label ClassificationonMIMIC-III
Precision@8· 2018-02-15
54.2
best: 77.7 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
ClassificationonIMDb Movie Reviews
AUC
0.84
ClassificationonTwitter Sentiment Analysis
AUC
0.9298

Computer Vision6 results

Person Re-IdentificationoneSports Sensors Dataset
LogLoss· 2020-11-02
0.01615
best: 0.01588 (SVM)
SOTA
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset arXiv:2011.00958
Skills EvaluationoneSports Sensors Dataset
LogLoss· 2020-11-02
0.596
best: 0.311 (SVM)
SOTA
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset arXiv:2011.00958
Person Re-IdentificationoneSports Sensors Dataset
Accuracy· 2020-11-02
48.8
best: 52.1 (Random Forest)
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset arXiv:2011.00958
Person Re-IdentificationoneSports Sensors Dataset
ROC AUC· 2020-11-02
0.884
best: 0.919 (Random Forest)
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset arXiv:2011.00958
Skills EvaluationoneSports Sensors Dataset
Accuracy· 2020-11-02
83.8
best: 85.6 (SVM)
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset arXiv:2011.00958
Skills EvaluationoneSports Sensors Dataset
ROC AUC· 2020-11-02
0.886
best: 0.945 (SVM)
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset arXiv:2011.00958

Medical6 results

Medical Code PredictiononMIMIC-III
Macro-AUC· 2018-02-15
56.1
best: 96.2 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Medical Code PredictiononMIMIC-III
Macro-F1· 2018-02-15
1.1
best: 24.7 (PLM-CA)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Medical Code PredictiononMIMIC-III
Micro-AUC· 2018-02-15
93.7
best: 99.3 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Medical Code PredictiononMIMIC-III
Micro-F1· 2018-02-15
27.2
best: 61.2 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Medical Code PredictiononMIMIC-III
Precision@15· 2018-02-15
41.1
best: 62.4 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695
Medical Code PredictiononMIMIC-III
Precision@8· 2018-02-15
54.2
best: 77.7 (GKI-ICD)
Explainable Prediction of Medical Codes from Clinical Text arXiv:1802.05695

Graphs2 results

Graph RegressiononTox21
AUC@80%Train
0.71
best: 0.78 (CensNet)
Graph RegressiononLipophilicity
RMSE@80%Train
1.15
best: 0.93 (CensNet)