Building A Neural Net

Kevin Feasel


R, Spark

Shirin Glander has a great post on using Spark + sparklyr + h2o + rsparkling to build a neural net to study arrhythmia of the heart:

The data I am using to demonstrate the building of neural nets is the arrhythmia dataset from UC Irvine’s machine learning database. It contains 279 features from ECG heart rhythm diagnostics and one output column. I am not going to rename the feature columns because they are too many and the descriptions are too complex. Also, we don’t need to know specifically which features we are looking at for building the models. For a description of each feature, see The output column defines 16 classes: class 1 samples are from healthy ECGs, the remaining classes belong to different types of arrhythmia, with class 16 being all remaining arrhythmia cases that didn’t fit into distinct classes.

Very interesting post.

Related Posts

Controlling Partition and File Counts in Spark

Landon Robinson shows how we can control the number of partitions (and therefore the number of output files) on reduce-style jobs in Spark: Whatever the case may be, the desire to control the number of files for a job or query is reasonable – within, ahem, reason – and in general is not too complicated. And, it’s often […]

Read More

Creating an Azure Databricks Cluster

Brad Llewellyn shows how you can create an Azure Databricks cluster: There are three major concepts for us to understand about Azure Databricks, Clusters, Code and Data.  We will dig into each of these in due time.  For this post, we’re going to talk about Clusters.  Clusters are where the work is done.  Clusters themselves […]

Read More


March 2017
« Feb Apr »