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What repositories for machine learning benchmarks do you know?
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In machine learning, benchmarking is the practice of comparing tools to identify the best-performing technologies in the industry. However, comparing different machine learning platforms can be a difficult task due to the large number of factors involved in the performance of a tool.
Benchmarks are datasets composed of tests and metrics to measure the performance of AI systems on specific tasks. An example is ImageNet, a popular benchmark for evaluating image classification systems. ImageNet contains millions of images labeled for more than a thousand categories.
Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging. The bootstrap is a powerful statistical method for estimating a quantity from a data sample. Such as a mean.
MLPerf appears to be the emerging industry/academia-backed ML benchmarking suite. The repo is here.
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Most machine learning systems are highly specific, and as such, a benchmark between them is not likely to be useful.
A system that works well at learning how to recognise spoken english (and thus typing up speech), would not neccessarily work well at finding the shortest path in the travelling salesperson problem.
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If you are looking specifically for Machine Learning applied to NLP, this is a very well curated resource:
http://nlpprogress.com/
Essentially, it's web page (github repo) to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
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