SOTAVerified

AutoML

Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)

Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms

Papers

Showing 7180 of 641 papers

TitleStatusHype
Problem-oriented AutoML in Clustering0
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning0
Towards Automated Machine Learning Research0
forester: A Tree-Based AutoML Tool in RCode2
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce ApplicationsCode1
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation0
Automated Machine Learning in InsuranceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified