# tfma.metrics.MeanSquaredLogarithmicError

Calculates the mean of squared logarithmic error.

Inherits From: `Metric`

Formula: error = L2_norm(log(label + 1) - log(prediction + 1))**2 Note: log of an array will be elementwise, i.e. log([x1, x2]) = [log(x1), log(x2)]

The metric computes the mean of squared logarithmic error (square of L2 norm) between labels and predictions. The labels and predictions could be arrays of arbitrary dimensions. Their dimension should match.

`name` The name of the metric.

`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.

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### `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"label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]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" }]