This sample demonstrates the power of simplification by implementing a binary classfier
using the popular Adult Census dataset, first with mmlspark library then comparing that with
the standad Spark ML constructs.
# Using MMLSpark to Classify Income Level
This sample demonstrates the power of simplification by implementing a binary classfier
using the popular Adult Census dataset, first with mmlspark library then comparing that with
the standad Spark ML constructs.
To learn more about mmlspark library, please visit: http://github.com/azure/mmlspark.
Run train_mmlspark.py in a local Docker container.
```
$ az ml experiment submit -c docker train_mmlspark.py 0.1
```
Create myvm.compute file to point to a remove VM
```
$ az ml computetarget attach --name <myvm> --address <ip address or FQDN> --username <username> --password <pwd>
```
Run train_mmlspark.py in a Docker container (with Spark) in a remote VM:
```
$ az ml experiment submit -c myvm train_mmlspark.py 0.3
```
Create myhdi.compute to point to an HDI cluster
```
$ az ml computetarget attach --name <myhdi> --address <ip address or FQDN of the head node> --username <username> --password <pwd> --cluster
```
Run it in a remote HDInsight cluster:
```
$ az ml experiment submit -c myhdi train_mmlspark.py 0.5
```
