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XEMP qualitative strength

XEMP-based Prediction Explanations provide a visual indicator of the qualitative strength of each explanation presented by the insight. In the API, these values are returned from the qualitativeStrength response parameter of the Prediction Explanation API endpoint.

The distribution is approximated from the validation data; the preview is computed on the validation data.

Score translations

The boundaries between indicators (for example, +++, ++, and +) are different when there are different numbers of features in a model. The tables below describe, based on feature count, how the calculations translate to the visual representation.

Some notes:

  • If an explanation’s score is trivial and has little or no qualitative effect, the output displays three grayed out symbols (+++ or ---). This indicates, for the represented directionality, that the effect is minor.

  • When there are a large number of features, a normalized score greater than 0.2 is represented as +++, so it is possible for multiple features to display this symbolic score in a single row.

In the tables, q represents the "qualitative" (or "normalized") score.

Features = 1

The following describes the displayed symbolic score based on the calculated qualitative score for models built with a single feature:

Qualitative Score Symbolic Score
q <= -0.001 ---
-0.001 < q <= 0 grayed-out ---
0 < q < 0.001 grayed-out +++
q >= 0.001 +++

Features = 2

The following describes the displayed symbolic score based on the calculated qualitative score for models built with two features:

Qualitative Score Symbolic Score
q < -0.75 ---
-0.75 <= q < -0.25 --
-0.25 <= q <= -0.001 -
-0.001 < q <= 0 grayed-out ---
0 < q < 0.001 grayed-out +++
0.001 <= q <= 0.25 +
0.25 < q <= 0.75 ++
q > 0.75 +++

Features >= 2, < 10

The following describes the displayed symbolic score based on the calculated qualitative score for models built with more than two but fewer than 10 features:

Qualitative Score Symbolic Score
q < -2 / num_features ---
-2 / num_features <= q < -1 / (2 * num_features) --
-1 / (2 * num_features) <= q <= -0.001 -
-0.001 < q <= 0 grayed-out ---
0 < q < 0.001 grayed-out +++
0.001 <= q <= 1 / (2 * num_features) +
1 / (2 * num_features) < q <= 2 / num_features ++
q > 2 / num_features +++

Features >= 10

The following describes the displayed symbolic score based on the calculated qualitative score for models built with 10 or more features:

Qualitative Score Symbolic Score
q < -0.2 ---
-0.2 <= q < -0.05 --
-0.05 <= q <= -0.001 -
-0.001 < q <= 0 grayed-out ---
0 < q < 0.001 grayed-out +++
0.001 <= q <= 0.05 +
0.05 < q <= 0.2 ++
q > 0.2 +++

Updated March 30, 2022
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