Feature Importance

Overview

Feature Importance measures how much each variable contributes to predicting a target. It fits a gradient boosting model and reports both built-in importance scores and permutation importance across multiple quantiles (Q10, Q25, Q50, Q75, Q90), giving you a sense of each variable's impact and the stability of that estimate.

How to Run

1. Open Feature Importance from the navigation.
2. Select Modeling Combinations — choose one or more Country/Region/Grouping triplets.
3. Select Targets — the variables you want to predict (defaults to the Sales group).
4. Select Variables — the candidate predictors (defaults to all variables except Sales and Events).
5. Click Get Feature Importance.

Reading the Results

Table

The results table shows one row per combination × target × variable with columns for each quantile (Q10–Q90). It is sorted by Q50 descending by default.

You can:

• Filter by combination, target, or variable group.
• Sort any column.
• Toggle columns with the Show/Hide Columns button.

Plots

Plots are organized into tabs by variable group (Media, Price, Distribution, etc.). Each tab contains scatter plots — one per target — showing the quantile spread for every variable.

Create Model Shortcut

Select rows in the table and click Create Model to jump directly into model building with those variables pre-selected. This is a convenient way to go from exploration to modeling.

Tips

• A high Q50 with a wide Q10–Q90 spread means the variable is important but its ranking is unstable — investigate further.
• Compare importance across multiple targets to find variables that are broadly predictive vs. target-specific.
• Use this as a pre-modeling step to reduce the variable set before building a model.

See Also

• Correlation — measure pairwise relationships between variables
• Models — build and manage models