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,