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 561570 of 641 papers

TitleStatusHype
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical EvolutionCode0
Evolutionary Architecture Search for Graph Neural NetworksCode0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for CzechiaCode0
Benchmark and Survey of Automated Machine Learning FrameworksCode0
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoMLCode0
Encoding high-cardinality string categorical variablesCode0
Manas: Mining Software Repositories to Assist AutoMLCode0
Automated Imbalanced LearningCode0
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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