Imagine that I am training a Spark MLlib model as follows: val traingData = loadTrainingData(...)val logisticRegression = new LogisticRegression()
traingData.cacheval logisticRegressionModel = logisticRegression.fit(trainingData) Does the call traingData.cache improve performances at training time or is it not needed? Does the .fit(...) method for a ML algorithm call cache/unpersist internally?
