> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Evaluation Metrics

The types of evaluation metrics used in measuring the accuracy of your predictive query will vary depending on your target type (i.e., what kind of prediction problem you're solving).

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## Binary Classification

If your predictive query is determining *one of two* values (e.g., true/false, yes/no), Kumo provides the following evaluation metrics:

* Accuracy

* Area Under The Receiver Operating Characteristic Curve (AUROC)

* Area Under The Precision-Recall Curve (AUPRC)

* Confusion Matrix

* Gain Chart

## Multiclass Classification

If your predictive query is distinguishing between *three or more* values (e.g., the `LAST`value from a high cardinality categorical column in the target table), Kumo provides the following evaluation metrics:

* Accuracy

## Multilabel Classification

If your predictive query returns *one or more* values from a list of categorical value candidates (e.g., the `LIST_DISTINCT` of values from a high cardinality categorical column in the target table), Kumo provides the following macro/micro/per-label versions of the following evaluation metrics:

* AUPRC

* AUROC

* AP

If your predictive query uses `LIST_DISTINCT` over a non-foreign-key categorical column, it will trigger a multilabel classification task.

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## Link Prediction

If your pQuery returns a list of distinct values associated with your entity (e.g. `LIST_DISTINCT`), Kumo provides top-k retrieval metrics (for K = 1, 10, and 100):

* F1\@K

* MAP\@K

* Precision\@K

* Recall\@K

Your pQuery is deemed a link prediction task if it uses `LIST_DISTINCT` over a foreign key column; otherwise it will trigger a multilabel classification task.

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## Regression

If your pQuery returns a numeric value (e.g. the `SUM/MAX`/etc. of values from a numerical column in the target table), Kumo provides the following metrics:

* Mean Absolute Error (MAE)

* Mean Squared Error (MSE)

* Root Mean Square Error (RMSE)

* Mean Absolute Percentage Error (MAPE)

* Symmetric Mean Absolute Percentage Error (SMAPE)

* Distribution of Predictions Histogram