## Uses of Interface
org.apache.ignite.ml.selection.scoring.metric.Metric

- Packages that use Metric
  Package | Description
  --- | ---
  org.apache.ignite.ml.selection.cv | Root package for cross-validation algorithms.
  org.apache.ignite.ml.selection.scoring.evaluator | Package for model evaluator classes.
  org.apache.ignite.ml.selection.scoring.metric | Package for metrics.
  org.apache.ignite.ml.selection.scoring.metric.classification | Package for classification metrics.
  org.apache.ignite.ml.selection.scoring.metric.regression | Package for regression metrics.

- - ### Uses of Metric in org.apache.ignite.ml.selection.cv

    Fields in org.apache.ignite.ml.selection.cv declared as Metric
    Modifier and Type | Field and Description
    --- | ---
    `protected Metric` | AbstractCrossValidation.`metric` Metric.

  - ### Uses of Metric in org.apache.ignite.ml.selection.scoring.evaluator

    Methods in org.apache.ignite.ml.selection.scoring.evaluator with parameters of type Metric
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V> EvaluationResult` | Evaluator.`evaluate(DatasetBuilder<K,V> datasetBuilder, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric... metrics)` Evaluate model.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.

  - ### Uses of Metric in org.apache.ignite.ml.selection.scoring.metric

    Methods in org.apache.ignite.ml.selection.scoring.metric that return Metric
    Modifier and Type | Method and Description
    --- | ---
    `Metric` | MetricName.`create()` Creates an instance of metric class by name.
    `Metric<L,C,A>` | Metric.`initBy(A aggr)` Initializes metric value by statistics aggregator.

  - ### Uses of Metric in org.apache.ignite.ml.selection.scoring.metric.classification

    Classes in org.apache.ignite.ml.selection.scoring.metric.classification that implement Metric
    Modifier and Type | Class and Description
    --- | ---
    `class` | `Accuracy<L extends Serializable>` Accuracy metric class.
    `class` | `BalancedAccuracy<L extends Serializable>` BalancedAccuracy metric class.
    `class` | `BinaryClassificationMetric<L extends Serializable>` Common abstract class for all binary classification metrics.
    `class` | `FallOut<L extends Serializable>` FallOut metric class.
    `class` | `FalseNegativeAbsoluteValue<L extends Serializable>` FalseNegative metric class.
    `class` | `FalsePositiveAbsoluteValue<L extends Serializable>` FalsePositive metric class.
    `class` | `Fdr<L extends Serializable>` FDR metric class.
    `class` | `FMeasure<L extends Serializable>` F-measure metric class.
    `class` | `MissRate<L extends Serializable>` MissRate metric class.
    `class` | `Npv<L extends Serializable>` NPV metric class.
    `class` | `Precision<L extends Serializable>` Precision metric class for binary classification.
    `class` | `Recall<L extends Serializable>` Recall metric class for binary classification.
    `class` | `Specificity<L extends Serializable>` Specificity metric class.
    `class` | `TrueNegativeAbsoluteValue<L extends Serializable>` TrueNegative metric class.
    `class` | `TruePositiveAbsoluteValue<L extends Serializable>` TruePositive metric class.

  - ### Uses of Metric in org.apache.ignite.ml.selection.scoring.metric.regression

    Classes in org.apache.ignite.ml.selection.scoring.metric.regression that implement Metric
    Modifier and Type | Class and Description
    --- | ---
    `class` | `Mae` Class for mean absolute error metric.
    `class` | `Mse` Class for mean squared error metric.
    `class` | `R2` Class for coefficient of determination metric.
    `class` | `Rmse` Class for RMSE metric.
    `class` | `Rss` Class for RSS metric (residual sum of squares).

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Release Date | : December 9 2020