Benchmarking rxNeuralNet for OCR

By David Smith

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

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:

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

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