Benchmarking rxNeuralNet for OCR

[This article was first published on Revolutions, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The MicrosoftML package introduced with Microsoft R Server 9.0 added several new functions for high-performance machine learning, including rxNeuralNet. Tomaz Kastrun recently applied rxNeuralNet to the MNIST database of handwritten digits to compare its performance with two other machine learning packages, h2o and xgboost. The results are summarized in the chart below:

RxNeuralNet

In addition to having the best performance (for both the CPU-enabled and GPU-enabled modes), rxNeuralNetwork did not have to sacrifice accuracy. In fact, rxNeuralNetwork had the best accuracy of the three algorithms: 97.8%, compared to 95.3% for h2o and 94.9% for xgBoost. The same training and validation set were used for each case, and the R code is available here. (If you're looking for other uses of MicrosoftML, this script also applies algorithms like rxFastForest and rxFastLinear to various other datasets.)

TomaztSQL: rxNeuralNet vs. xgBoost vs. H2O

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)