In this hands-on tutorial you will build and evaluate machine learning models by using the AutoAI feature in Watson Studio.
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This tutorial consists of 6 parts, you can start with part I or any other part, however, the necessary environment is set up in part I.
Part I – data visualization, preparation, and transformation
Part II – build and evaluate machine learning models by using AutoAI
Part III – graphically build and evaluate machine learning models by using SPSS Modeler flow
Part IV – set up and run Jupyter Notebooks to develop a machine learning model
Part V – deploy a local Python app to test your model
Part VI – monitor your model with OpenScale
AutoAI is a feature in Watson Studio that you can use to develop machine learning models fast and easily. In a first step you choose your data set – for instance for customer churn – and select what you would like to predict.
Then you can run the experiment. As the experiment is run, you see the different pipelines in the relationship map. After it finishes, a list of completed models is listed at the bottom of the panel, in order of accuracy. You can also take a look at the Progress map, by clicking swap view.
In the next step you can evaluate the model performance of the best models listed in the pipeline leaderboard.
Inside the Model Evaluation window, there is a menu on the left that provides more metrics for the pipeline, such as: Confusion Matrix table or Feature Importance graph.
Afterwards you can save your pipeline as a new model or as a notebook.
To access the complete tutorial go to this GitHub Repository.