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Erin LeDell: Happy to share our #ICML2019 #AutoML Workshop paper, "An Open Source AutoML Benchmark". We present a new #opensource AutoML benchmarking system and include results on: H2O AutoML, auto-sklearn, TPOT, Auto-WEKA
📰 Paper: https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf
👩💻 Code: https://github.com/openml/automlbenchmark/
✴️ @AI_Python_EN
📰 Paper: https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf
👩💻 Code: https://github.com/openml/automlbenchmark/
✴️ @AI_Python_EN
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Hey #DeepLearning #AI enthusiast, have you heard of this cool drag & drop AI from #DSAIL lab from MIT?
This is an amazing tool which #managers and #datascience professionals can use instantly!
The researchers evaluated the tool on 300 real-world datasets. Compared to other state-of-the-art #AutoML systems, VDS’ approximations were as accurate, but were generated within seconds, which is much faster than other tools, which operate in minutes to hours.
Next they want to add features like alerts users to potential data bias or errors. For example, to protect patient privacy, sometimes researchers will label medical datasets with patients aged 0 (if they do not know the age) and 200 (if a patient is over 95 years old). But beginners may not recognize such errors, which could completely throw off their analytics.
Here is link to their project Northstar
https://lnkd.in/dmHQugW
Take a look! This is pretty awesome.
#artificialintelligence #automation #autoML #visualization
✴️ @AI_Python_EN
This is an amazing tool which #managers and #datascience professionals can use instantly!
The researchers evaluated the tool on 300 real-world datasets. Compared to other state-of-the-art #AutoML systems, VDS’ approximations were as accurate, but were generated within seconds, which is much faster than other tools, which operate in minutes to hours.
Next they want to add features like alerts users to potential data bias or errors. For example, to protect patient privacy, sometimes researchers will label medical datasets with patients aged 0 (if they do not know the age) and 200 (if a patient is over 95 years old). But beginners may not recognize such errors, which could completely throw off their analytics.
Here is link to their project Northstar
https://lnkd.in/dmHQugW
Take a look! This is pretty awesome.
#artificialintelligence #automation #autoML #visualization
✴️ @AI_Python_EN