SAP

Author

Cindy Venet

Cindy Venet

Cindy Venet is the Documentation Lead for Smart Predict and SAP Predictive Analytics. She started at SAP first as a project manager for the SAP Language Services, before moving to User Assistance in 2014. Her role has been to coordinate the communication between multiple teams with the aim to deliver high-quality user assistance for our products. For any feedback regarding the Smart Predict and Predictive Analytics documentation, contact Cindy

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Smart Predict is an Augmented Analytics feature in SAP Analytics Cloud that helps you generate predictions about future events, values, and trends.

The predictive experience in SAP Analytics Cloud is simple. Smart Predict guides you step by step to create a predictive model based on historical data. The resulting model can be used to make trusted future predictions, providing you with advanced insights to guide decision making.

Before using Smart Predict for the first time, it really helps to understand a few basic concepts of predictive modeling. So, here they are!

The different types of predictive scenarios

There are currently 3 types of predictive scenarios available in Smart Predict:

  • Classification
  • Regression
  • Time Series

Defining the business problem or business question you want to address will help you choose the right type of predictive scenario.

Watch this for an introduction to the types of predictive scenarios.

This playlist contains several different example use cases.

Our three datasets in a nutshell

To create and manage predictive scenarios in Smart Predict, you need a few different datasets:

  • The training dataset contains the historical data your predictive model will learn from. In this dataset, the values for your target variable, which is the column related to your business question, are known.
  • The application dataset contains current or new data that you would like to create predictions for. In this dataset, the values for the target variable are unknown.
  • The output dataset contains your predictions and any additional columns that you have requested.

Watch this video for an overview of the datasets.

If you want to know more, check out our Smart Predict documentation.

What are the variables and roles?

Variables are the column values in your dataset. You need to assign roles to different variables to create a predictive model:

Role Description Notes
Target or Signal variable This is the answer to your business question Target variable is used for Classification and regression whereas Signal variable is used for Time Series Forecasting
Date variable This is the time dimension This variable is mandatory for a Time Series Forecasting predictive scenario
Segmented variable This allows you to divide your dataset into several subsections leading to more customized predictions This variable is only used in a Time Series Forecasting predictive scenario
Excluded variable These are variables to ignore in the predictive model
Influencer Variables These variables are other data points that will be used to explain the target variable

Watch this video to learn more about variables:

For more detailed information, check our Smart Predict documentation.

About training and debriefing

Once you have specified a training dataset and identified the required variables you can generate a predictive model.

To train a predictive model, Smart Predict splits your dataset into 2 subsets. It generates several predictive models using the first subset and applies each version of the predictive model against the second subset to test for accuracy and robustness. The best performing version becomes your selected predictive model.

The debriefing stage is where you assess the selected predictive model to decide whether or not the model is ready for use. At this point, you can decide to apply the model to generate predictions, improve the model by adding or removing data, or create a new model from scratch.

Watch this for an overview of the predictive model training and debriefing stages.

You can find even more information in our documentation:

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