apache-spark-mllib

I do not understand the output of the SVM classifier from the Spark MLLib algorithm. I want to convert the score to a probability, so that I get a probability for a data-point belonging to a certain class (on which the SVM is trained, a.k.a. multi-class problem) (see also this thread) . It is unclear what the score means. Is it the distance to the hyperplane? How do I get the probabilities from it? import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD} import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.util.MLUtils // Load training

I am new for learning Spark MLlib. When I was reading about the example of Binomial logistic regression, I don't understand the format type of "libsvm". ( Binomial logistic regression ) The text looks like: 0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271

I have two separate DataFrames which each have several differing processing stages which I use mllib transformers in a pipeline to handle. I now want to join these two pipelines together, keeping the features (columns) from each DataFrame . Scikit-learn has the FeatureUnion class for handling this, and I can't seem to find an equivalent for mllib . I can add a custom transformer stage at the end of one pipeline that takes the DataFrame produced by the other pipeline as an attribute and join it in the transform method, but that seems messy. Pipeline or PipelineModel are valid PipelineStages ,

I am using Spark Mlib for kmeans clustering. I have a set of vectors from which I want to determine the most likely cluster center. So I will run kmeans clustering training on this set and select the cluster with the highest number of vector assigned to it. Therefore I need to know the number of vectors assigned to each cluster after training (i.e KMeans.run(...)). But I can not find a way to retrieve this information from KMeanModel result. I probably need to run predict on all training vectors and count the label which appear the most. Is there another way to do this? Thank you You are right

I am trying to find Accuracy using 5-fold cross validation using Random Forest Classifier Model in SCALA. But i am getting the following error while running: java.lang.IllegalArgumentException: RandomForestClassifier was given input with invalid label column label, without the number of classes specified. See StringIndexer. Getting the above error at line---> val cvModel = cv.fit(trainingData) The code which i used for cross validation of data set using random forest is as follows: import org.apache.spark.ml.Pipeline import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator} import

This question already has an answer here: How to convert ArrayType to DenseVector in PySpark DataFrame? 1 answer I want to change List to Vector in pySpark, and then use this column to Machine Learning model for training. But my spark version is 1.6.0, which does not have VectorUDT() . So what type should I return in my udf function? from pyspark.sql import SQLContext from pyspark import SparkContext, SparkConf from pyspark.sql.functions import * from pyspark.mllib.linalg import DenseVector from pyspark.mllib.linalg import Vectors from pyspark.sql.types import * conf = SparkConf().setAppName(