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ML.INSPECT.FEATURE_IMPORTANCES

Returns the feature importances of a fitted tree-ensemble model.

Syntax

ML.INSPECT.FEATURE_IMPORTANCES(model)

Arguments

Name Type Default Description
model object Fitted tree-ensemble model with a feature_importances_ attribute (e.g. created by ML.CLASSIFICATION.RANDOM_FOREST_CLF and trained with ML.FIT).

Returns

A DataFrame with columns [feature, importance], one row per training feature.

When to use

Use ML.INSPECT.FEATURE_IMPORTANCES to read the per-feature feature_importances_ score from a fitted tree-ensemble model (Random Forest, Extra Trees, gradient boosting, etc.). Higher values indicate features the model relied on more heavily when choosing where to split the data.

A common use is to rank predictors after a one-shot fit: train the model on the full feature set, pull the importances back into Excel, and chart them as a bar chart to see which inputs carry the predictive signal.

Examples

Train a Random Forest on the data in A2:K100 (predictors) and L2:L100 (target), then read the importances:

=ML.CLASSIFICATION.RANDOM_FOREST_CLF(100, "gini", , 2, 1, 1, TRUE, 0)
=ML.FIT(N1, A2:K100, L2:L100)
=ML.INSPECT.FEATURE_IMPORTANCES(N2)

Pair the result with a bar chart that uses the feature column as categories and the importance column as values to get a ranked bar chart.

Remarks

  • The model passed in must already be fitted and must expose a feature_importances_ attribute. Linear models (LinearRegression, LogisticRegression, Ridge, Lasso) expose coef_ instead, not feature_importances_, so they will be rejected. For coefficients, use ML.INSPECT.COEFFICIENTS once available.
  • When the model was fitted on a DataFrame, the feature column uses the original column names. When fitted on an unnamed array, it falls back to feature_0, feature_1, … in input order.
  • Tree-ensemble importances are impurity-based (mean decrease in impurity). They favour high-cardinality features and can be misleading when features are strongly correlated. For comparing across pipelines, consider permutation importance or SHAP — neither is exposed in this add-in yet.
  • Importances sum to ~1.0 for a standard Random Forest. Use the relative ranking, not the absolute value, when comparing across models.

See also