There are multiple numeric fields which are predictacle, for this example we will try to predict a person’s age based on the other input characteristics. I will use the ADULT01 table for this example which is a dataset of persons with some properties on their education, marital status, relationship, etc. After importing you should see about 10 sample tables installed which cater to different use cases. By default these will be loaded in their own schema named ‘APL_SAMPLES’.
Navigate to the ‘samples/data’ folder to import the sample tables into your system. Now open your Eclipse / HANA Studio and go to File -> Import and choose Delivery Unit. After extracting the package you should be able to see a ‘samples/data’ folder which contains the samples to be imported into HANA.
#CREATING ROLES IN SAP HANA STUDIO DOWNLOAD#
Go to ‘Software downloads’ and type ‘APL’ to download a recent version. Installing example dataįor the debrief example I will be using one of the bundled APL examples from the package you can download from. It is therefore extremely helpful in communicating its forecast results to business users. The Advanced Predictive Library allows to view the exact influencers of each model and allows to decompose a prediction into the components of the various variables. For instance if a prediction is off by 300% it is valuable to learn which specific variable was the root cause for the wrong prediction. Now what is exactly meant by this? A common perception is that a machine learning model is a “black box” generating predictions from which it is very difficult to obtain the root cause telling why a specific prediction was made. The APL regression modeling algorithm uses a gradient boosting tree under the hood which is extremely well suited for this purpose. Explainable AIīeing able to explain the inner workings of a model (also “explainable AI” or “XAI”) is an important topic in applying machine learning in enterprise environments and is recently gaining much attention.
In this part of the blog post I will dive deeper into built-in functionality to retrieve insights into a trained model which is called the ‘model debrief’. In part 1 I have outlined a way to utilize the APL library for in-database training of a regression model in HANA in order to be used together with an external Node.js inference script.
#CREATING ROLES IN SAP HANA STUDIO SERIES#
This is part 2 in a two-part series of blog posts on large-scale and explainable forecasting using APL.
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