我使用Apache Spark MLLib库实现了一些机器学习算法 . 我使用MulticlassClassificationEvaluator对象获得结果 . 我想得到的结果是精确度,召回率和准确度 .

问题是我使用的所有算法的准确度和召回值是相同的 . 例如,随机森林的准确度和召回值为98%,朴素贝叶斯算法的准确率和召回率为%95 . 我使用的其他算法的情况也是如此 . 这是正常的吗?它与我获得结果的方式有关吗?

这是我使用的一些实现 . 随机森林:

Dataset<Row> dataFrame              = sparkBase
            .getSpark()
            .read()
            .format("libsvm")
            .load(svFilePath);
    StringIndexerModel labelIndexer     = new StringIndexer()
            .setInputCol("label")
            .setOutputCol("indexedLabel")
            .fit(dataFrame);
    VectorIndexerModel featureIndexer   = new VectorIndexer()
            .setInputCol("features")
            .setOutputCol("indexedFeatures")
            .setMaxCategories(categoryCount)
            .fit(dataFrame);

    Dataset<Row>[] splits = dataFrame.randomSplit(new double[]
                {mainController.getTrainingDataRate(), mainController.getTestDataRate()}, 1234L);
        Dataset<Row> train = splits[0];
        Dataset<Row> test = splits[1];

        RandomForestClassifier rf = new RandomForestClassifier()
                .setLabelCol("indexedLabel")
                .setFeaturesCol("indexedFeatures");

        IndexToString labelConverter = new IndexToString()
                .setInputCol("prediction")
                .setOutputCol("predictedLabel")
                .setLabels(labelIndexer.labels());

        Pipeline pipeline = new Pipeline()
                .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter});

        PipelineModel model = pipeline.fit(train);

        Dataset<Row> predictions = model.transform(test);
        MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
                .setLabelCol("indexedLabel")
                .setPredictionCol("prediction")
                .setMetricName("accuracy");
        accuracy = evaluator.evaluate(predictions);

        evaluator.setMetricName("weightedRecall");
        recall = (evaluator.evaluate(predictions));

        evaluator.setMetricName("weightedPrecision");
        precision = (evaluator.evaluate(predictions));

朴素贝叶斯算法:

Dataset<Row> dataFrame =         sparkBase.getSpark().read().format("libsvm").load(svFilePath);
    Dataset<Row>[] splits = dataFrame.randomSplit(new double[]
            {mainController.getTrainingDataRate(), mainController.getTestDataRate()}, 1234L);
    Dataset<Row> train = splits[0];
    Dataset<Row> test = splits[1];

    NaiveBayes nb = new NaiveBayes();
    NaiveBayesModel model = nb.fit(train);

        Dataset<Row> predictions = model.transform(test);

        MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
                .setLabelCol("label")
                .setPredictionCol("prediction")
                .setMetricName("weightedPrecision");

        precision = (evaluator.evaluate(predictions));

        evaluator.setMetricName("weightedRecall");
        recall = (evaluator.evaluate(predictions));

        evaluator.setMetricName("accuracy");
        accuracy = (evaluator.evaluate(predictions));

难道我做错了什么?问候,