Build RMSE evaluator

Now that you know how to fit a model to training data and generate test predictions, you need a way to evaluate how well your model performs. For this we'll build an evaluator. Evaluators in Spark can be built out in various ways. For our purposes, we want a regressionEvaluator that calculates the RMSE. After we build our regressionEvaluator, we can fit the model to our data and generate predictions.

Import the required RegressionEvaluator package from the pyspark.ml.evaluation class.
Complete the evaluator code, specifying the metric name to be "rmse". Set the labelCol to the name of the column in our ratings data that contains our ratings (use the ratings.columns method to see column names) and set the prediction column name to "prediction".
Confirm that the evaluator was properly created by extracting each of the three parameters from it. Do this by running the following 3 lines of code, each within a print statement:
- evaluator.getMetricName()
- evaluator.getLabelCol()
- evaluator.getPredictionCol()

Exercise

Build RMSE evaluator

Instructions