The purpose of this example is to demonstrate how to use a feature selection technique not available for Azure ML experiments.
# Classifying Iris
This is a companion sample project of the Iris tutorial that you can find from the main GitHub documentation site. Using the timeless [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), it walks you through the basics.
Enjoy!
## Quick CLI references
If you want to try quickly from the command line window launched from the _File_ menu:
Kick-off many local runs to observe the metrics emitted by each run in a graph.
```
# Only needed if you don't have matplotlib installed
$ pip install matplotlib
# kick off many local runs sequentially
$ python run.py
```
Run _iris_sklearn.py_ in local environment.
```
$ az ml experiment submit -c local iris_sklearn.py
```
Run _iris_sklearn.py_ in a local Docker container.
```
$ az ml experiment submit -c docker-python iris_sklearn.py
```
Run _iris_pyspark.py_ in a local Docker container.
```
$ az ml experiment submit -c docker-spark iris_pyspark.py
```
Create _myvm.compute_ file to point to a remote VM
```
$ az ml computetarget attach --name <myvm> --address <ip address or FQDN> --username <username> --password <pwd> --type remotedocker
```
Run _iris_pyspark.py_ in a Docker container (with Spark) in a remote VM:
```
$ az ml experiment submit -c myvm iris_pyspark.py
```
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 iris_pyspark.py
```
