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Ensembles |
<a href="ml-guide.html">ML</a> - Ensembles |
Table of Contents
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An ensemble method
is a learning algorithm which creates a model composed of a set of other base models.
The Pipelines API supports the following ensemble algorithms: OneVsRest
OneVsRest is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently.
OneVsRest is implemented as an Estimator. For the base classifier it takes instances of Classifier and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.
Predictions are done by evaluating each binary classifier and the index of the most confident classifier is output as label.
The example below demonstrates how to load the
Iris dataset, parse it as a DataFrame and perform multiclass classification using OneVsRest. The test error is calculated to measure the algorithm accuracy.
val sqlContext = new SQLContext(sc)
// parse data into dataframe val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt") val Array(train, test) = data.toDF().randomSplit(Array(0.7, 0.3))
// instantiate multiclass learner and train val ovr = new OneVsRest().setClassifier(new LogisticRegression)
val ovrModel = ovr.fit(train)
// score model on test data val predictions = ovrModel.transform(test).select("prediction", "label") val predictionsAndLabels = predictions.map {case Row(p: Double, l: Double) => (p, l)}
// compute confusion matrix val metrics = new MulticlassMetrics(predictionsAndLabels) println(metrics.confusionMatrix)
// the Iris DataSet has three classes val numClasses = 3
println("label\tfpr\n") (0 until numClasses).foreach { index => val label = index.toDouble println(label + "\t" + metrics.falsePositiveRate(label)) } {% endhighlight %}
import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.OneVsRest; import org.apache.spark.ml.classification.OneVsRestModel; import org.apache.spark.mllib.evaluation.MulticlassMetrics; import org.apache.spark.mllib.linalg.Matrix; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.rdd.RDD; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.SQLContext;
SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample"); JavaSparkContext jsc = new JavaSparkContext(conf); SQLContext jsql = new SQLContext(jsc);
RDD data = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_multiclass_classification_data.txt");
DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class); DataFrame[] splits = dataFrame.randomSplit(new double[]{0.7, 0.3}, 12345); DataFrame train = splits[0]; DataFrame test = splits[1];
// instantiate the One Vs Rest Classifier OneVsRest ovr = new OneVsRest().setClassifier(new LogisticRegression());
// train the multiclass model OneVsRestModel ovrModel = ovr.fit(train.cache());
// score the model on test data DataFrame predictions = ovrModel .transform(test) .select("prediction", "label");
// obtain metrics MulticlassMetrics metrics = new MulticlassMetrics(predictions); Matrix confusionMatrix = metrics.confusionMatrix();
// output the Confusion Matrix System.out.println("Confusion Matrix"); System.out.println(confusionMatrix);
// compute the false positive rate per label System.out.println(); System.out.println("label\tfpr\n");
// the Iris DataSet has three classes int numClasses = 3; for (int index = 0; index < numClasses; index++) { double label = (double) index; System.out.print(label); System.out.print("\t"); System.out.print(metrics.falsePositiveRate(label)); System.out.println(); } {% endhighlight %}