using map(actions introducing mllib some mllib capabilities special mllib data types the vector data type labeledpoint data type rating data type decision trees in spark with mllib exploring decision trees code creating the sparkcontext importing and cleaning our data creating test candidate and building our decision tree running the script -means clustering in spark within set sum of squared errors (wssserunning the code tf-idf tf-idf in practice using tfidf searching wikipedia with spark mllib import statements creating the initial rdd creating and transforming hashingtf object computing the tf-idf score using the wikipedia search engine algorithm running the algorithm using the spark dataframe api for mllib how spark mllib works implementing linear regression summary testing and experimental design / testing concepts / tests measuring conversion for / testing how to attribute conversions variance is your enemy -test and -value the -statistic or -test the -value vii