Difference between revisions of "Benchmarks"
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| − | [http://www.youtube.com/results?search_query=~Benchmark+Benchmarking+machine+learning+Model | + | [http://www.youtube.com/results?search_query=~Benchmark+Benchmarking+machine+learning+Model YouTube search...] |
| − | [http://www.google.com/search?q=~Benchmark+Benchmarking+machine+learning+Model | + | [http://www.google.com/search?q=~Benchmark+Benchmarking+machine+learning+Model ...Google search] |
* [[Evaluation Measures - Classification Performance]] - [[Evaluation Measures - Classification Performance#Accuracy|Accuracy]], [[Evaluation Measures - Classification Performance#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]], and [[Evaluation Measures - Classification Performance#Specificity|Specificity]] | * [[Evaluation Measures - Classification Performance]] - [[Evaluation Measures - Classification Performance#Accuracy|Accuracy]], [[Evaluation Measures - Classification Performance#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]], and [[Evaluation Measures - Classification Performance#Specificity|Specificity]] | ||
Revision as of 12:51, 24 December 2019
YouTube search... ...Google search
- Evaluation Measures - Classification Performance - Accuracy, Precision & Recall (Sensitivity), and Specificity
- Datasets
- Gaming
- Machine Learning Benchmarks and AI Self-Driving Cars | Lance Eliot - AItrends
- Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science
- DAWNBench | Stanford - an End-to-End Deep Learning Benchmark and Competition
- Benchmarking 20 Machine Learning Models Accuracy and Speed | Marc Borowczak - Data Science Central
- Benchmarking deep learning models on large healthcare datasets | S. Purushotham, C. Meng, Z. Chea, and Y. Liu
GLUE
The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. GLUE consists of: A benchmark of nine sentence- or sentence-pair language understanding tasks built on established existing datasets and selected to cover a diverse range of dataset sizes, text genres, and degrees of difficulty, A diagnostic dataset designed to evaluate and analyze model performance with respect to a wide range of linguistic phenomena found in natural language, and A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set.
