10. Compute historical feature values

To prepare training and testing data (also called "Feature Table" in FeatureByte) for your Machine Learning models, follow these steps:

Step 1: Select a Feature List¶

Navigate to the Feature List catalog from the Experiment section of the menu.

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Select a desired Feature List and click on the calculator icon.

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Step 2: Select the Observation Table¶

Pick the Observation Table that specifies your entity values and historical points-in-time.

For instance, choose the table "In_Store_Customer_x_ProductGroup_Spending_next_2_weeks_2022_10K" we created previously. This table also includes the target values for "CUSTOMER_x_PRODUCTGROUP_Sum_of_TotalCost_next_2_weeks".

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Step 3: Name Your Feature Table¶

Assign a name to your feature table and, if you wish, provide a description. Here, we called the feature table: "In_Store_Customer_x_ProductGroup_Spending_next_2_weeks_2022_10K wtih 15 features".

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Step 4: Locate the Completed Table¶

After the table is computed, find your table in the 'Feature Table Catalog' under the 'Experiment' section.

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Step 5: Preview or Download¶

Select the "In_Store_Customer_x_ProductGroup_Spending_next_2_weeks_2022_10K wtih 15 features"

Go to the 'Preview' tab to preview the table.

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Go to the 'About' tab to download it as a parquet file.

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For SDK Users

More details are available in our SDK reference for Historical Feature Table.

With the SDK, you can directly compute training data as a Pandas DataFrame or convert a stored Feature Table into a Pandas DataFrame.