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What is a Predictive Query?

A Predictive Query is a declarative syntax used to define a predictive modeling task in Kumo. It specifies the target variable to predict and the data context for training. Kumo uses the Predictive Query Language (PQL), a SQL-like language, to automate all major steps of the ML pipeline, including feature engineering, generating training table, and training a model.

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Creating a Predictive Query

To train a model in Kumo:
  1. Navigate to New > Model from the side menu.
  2. On the Model Training page, enter a Model Name and optional Description.
  3. Select a Graph from your previously created graphs. Once a graph is selected, its structure and linkages appear on the right side for reference.
  4. Write your Predictive Query (PQL) in the text area.
Example PQL 
PREDICT COUNT(transactions.*, 0, 30, days) = 0
FOR EACH customers.customer_id
WHERE COUNT(transactions.*, -90, 0, days) > 0

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Model Settings

Before training, users can access advanced model settings to:
  • Configure baseline and run mode.
  • Adjust hyperparameters in the model plan.
  • Modify training table generation setting.
By default, Kumo optimizes the model automatically, but advanced settings allow customization based on specific needs.