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Model training and hyperparameter tuning

Once we have rowRDD and the header, the next task is to construct the rows of our Schema DataFrame from the variants using the header and rowRDD:

val sqlContext = spark.sqlContextval schemaDF = sqlContext.createDataFrame(rowRDD, header)schemaDF.printSchema()schemaDF.show(10)>>>

Figure 15: A snapshot of the training dataset containing features and the label (that is, Region) columns

In the preceding DataFrame, only a few columns, including the label, are shown so that it fits on the page.

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