spark技术分享BinaryClassificationEvaluator — Evaluator of Binary Classification Models
BinaryClassificationEvaluator is an Evaluator of cross-validate models from binary classifications (e.g. LogisticRegression, RandomForestClassifier, NaiveBayes, DecisionTreeClassifier, MultilayerPerceptronClassifier, GBTClassifier, LinearSVC).
BinaryClassificationEvaluator finds the best model by maximizing the model evaluation metric that is the area under the specified curve (and so isLargerBetter is turned on for either metric).
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import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator val binEval = new BinaryClassificationEvaluator(). setMetricName("areaUnderROC"). setRawPredictionCol("rawPrediction"). setLabelCol("label") scala> binEval.isLargerBetter res0: Boolean = true scala> println(binEval.explainParams) labelCol: label column name (default: label) metricName: metric name in evaluation (areaUnderROC|areaUnderPR) (default: areaUnderROC) rawPredictionCol: raw prediction (a.k.a. confidence) column name (default: rawPrediction) |
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Name of the classification metric for evaluation Can be either |
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Column name with raw predictions (a.k.a. confidence) |
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Name of the column with indexed labels (i.e. |
Evaluating Model Output — evaluate Method
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evaluate(dataset: Dataset[_]): Double |
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Note
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evaluate is part of Evaluator Contract.
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evaluate…FIXME
