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# tfma.metrics.CoefficientOfDiscrimination

Coefficient of discrimination metric.

Inherits From: `Metric`

The coefficient of discrimination measures the differences between the average prediction for the positive examples and the average prediction for the negative examples.

The formula is: AVG(pred | label = 1) - AVG(pred | label = 0) More details can be found in the following paper: https://www.tandfonline.com/doi/abs/10.1198/tast.2009.08210

`name` Metric name.

`compute_confidence_interval` Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.

## Methods

### `computations`

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Creates computations associated with metric.

### `get_config`

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Returns serializable config.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]