zero-one.geni.ml.evaluation
binary-classification-evaluatorclj
(binary-classification-evaluator params)Evaluator for binary classification, which expects input columns rawPrediction, label and an optional weight column. The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).
Timestamp: 2020-10-19T01:56:00.765Z
Evaluator for binary classification, which expects input columns rawPrediction, label and an optional weight column. The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities). Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.html Timestamp: 2020-10-19T01:56:00.765Z
clustering-evaluatorclj
(clustering-evaluator params)Evaluator for clustering results. The metric computes the Silhouette measure using the specified distance measure.
The Silhouette is a measure for the validation of the consistency within clusters. It ranges between 1 and -1, where a value close to 1 means that the points in a cluster are close to the other points in the same cluster and far from the points of the other clusters.
Timestamp: 2020-10-19T01:56:01.116Z
Evaluator for clustering results. The metric computes the Silhouette measure using the specified distance measure. The Silhouette is a measure for the validation of the consistency within clusters. It ranges between 1 and -1, where a value close to 1 means that the points in a cluster are close to the other points in the same cluster and far from the points of the other clusters. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.html Timestamp: 2020-10-19T01:56:01.116Z
multiclass-classification-evaluatorclj
(multiclass-classification-evaluator params)Evaluator for multiclass classification, which expects input columns: prediction, label, weight (optional) and probability (only for logLoss).
Timestamp: 2020-10-19T01:56:01.471Z
Evaluator for multiclass classification, which expects input columns: prediction, label, weight (optional) and probability (only for logLoss). Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.html Timestamp: 2020-10-19T01:56:01.471Z
multilabel-classification-evaluatorclj
(multilabel-classification-evaluator params):: Experimental :: Evaluator for multi-label classification, which expects two input columns: prediction and label.
Timestamp: 2020-10-19T01:56:01.814Z
:: Experimental :: Evaluator for multi-label classification, which expects two input columns: prediction and label. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/evaluation/MultilabelClassificationEvaluator.html Timestamp: 2020-10-19T01:56:01.814Z
ranking-evaluatorclj
(ranking-evaluator params):: Experimental :: Evaluator for ranking, which expects two input columns: prediction and label.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/evaluation/RankingEvaluator.html
Timestamp: 2020-10-19T01:56:02.374Z
:: Experimental :: Evaluator for ranking, which expects two input columns: prediction and label. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/evaluation/RankingEvaluator.html Timestamp: 2020-10-19T01:56:02.374Z
regression-evaluatorclj
(regression-evaluator params)Evaluator for regression, which expects input columns prediction, label and an optional weight column.
Timestamp: 2020-10-19T01:56:02.721Z
Evaluator for regression, which expects input columns prediction, label and an optional weight column. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.html Timestamp: 2020-10-19T01:56:02.721Z
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