在 Spark Mllib(F 分数、AUROC、AUPRC 等)中训练随机森林二元分类器模型时,我们如何获得模型指标?
问题是 BinaryClassificationMetrics
采用概率,而 RandomForest 分类器的 predict 方法返回离散值 0 或 1。
见:https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#binary-classification
一个 RandomForest.trainClassifier
没有任何clearThreshold
使其返回概率而不是离散的 0 或 1 标签的方法。
最佳答案
我们需要使用新的ml
基于 DataFrames 的 API 来获取概率,而不是基于 RDD mllib
API。
更新
以下是 Spark 文档中使用 BinaryClassificationEvaluator
的更新示例并显示指标:Area Under Receiver Operating Characteristic
(AUROC) 和 Area Under Precision Recall Curve
(AUPRC)。
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
// Load and parse the data file, converting it to a DataFrame.
val data = sqlContext.read.format("libsvm").load("D:/Sources/spark/data/mllib/sample_libsvm_data.txt")
// Index labels, adding metadata to the label column.
// Fit on whole dataset to include all labels in index.
val labelIndexer = new StringIndexer()
.setInputCol("label")
.setOutputCol("indexedLabel")
.fit(data)
// Automatically identify categorical features, and index them.
// Set maxCategories so features with > 4 distinct values are treated as continuous.
val featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.setMaxCategories(4)
.fit(data)
// Split the data into training and test sets (30% held out for testing)
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
// Train a RandomForest model.
val rf = new RandomForestClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
.setNumTrees(10)
// Convert indexed labels back to original labels.
val labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels)
// Chain indexers and forest in a Pipeline
val pipeline = new Pipeline()
.setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))
// Train model. This also runs the indexers.
val model = pipeline.fit(trainingData)
// Make predictions.
val predictions = model.transform(testData)
// Select example rows to display.
predictions
.select("indexedLabel", "rawPrediction", "prediction")
.show()
val binaryClassificationEvaluator = new BinaryClassificationEvaluator()
.setLabelCol("indexedLabel")
.setRawPredictionCol("rawPrediction")
def printlnMetric(metricName: String): Unit = {
println(metricName + " = " + binaryClassificationEvaluator.setMetricName(metricName).evaluate(predictions))
}
printlnMetric("areaUnderROC")
printlnMetric("areaUnderPR")
关于scala - Spark 随机森林二元分类器指标,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/37566321/