Automated Machine Learning with H2O Driverless AI

Hi everyone , this time I want to evaluate another automated machine learning tool called H2O Driverless AI and also compare it with DataRobot (of course a very lightweight type of comparison analysis has been done).

First great feature of H2O driverless AI is that you can have it instantly (almost) as long you have an Amazon, Google or Azure account you can spin up a H2O Driverless quite easily as described here  :

azure_vm_size

You can choose if you want to Bring your own license (you can ask an evaluation of 21 days as I did) or pay the cost of the license inside the hourly costs of the VM in your cloud provider.

Once you have your VM up & running, my suggestion is to update it to the latest docker image of H2O Driverless AI as described into the how to

sudo h2oai update

and then connect to the UI.

Once connected you can upload directly from the UI the datasets you like and perform ML experiments with them choosing which column you want predict/analyze and what is the metric you want to measure your model (AUC, etc..).

Here one running on 4 GPUs:

Screen Shot 2018-03-22 at 9.23.38 AM

Once experiment is finished with the interpreting model page you can understand the key influencers in your dataset for the target column you were interested to analyze/predict.

viewing_results

Since few days ago I did some test with DataRobot and Kaggle competitions I tried to perform the same on H2O and the results are….

Titanic Competition (metric accuracy –> higher better):

DataRobot 0.79904 (Best)

H2O 0.78947

House Prices Regression (metric RMSE –> lower better):

DataRobot 0.12566 (Best)

H2O 0.13378

As you can see on both DataRobot leads but the results of H2O are not so far away !

Talking instead of model understanding and explicability of the results in “human” terms I see DataRobot offering more different and meaningful visualizations than H2O, additionally you can decide by yourself which of the many models you want to use and not only the winning one (there are cases where you want to use one that is less accurate but that has a higher evaluation (inference) speed), while with H2O you have no choice than using the only one surviving to the process of automatic evaluation.

H2O however is more accessible in terms of testing/trying , it offers GPU acceleration that is a very nice bonus especially on large datasets .

Happy Auto ML !

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