In the next two chapters you'll step through every stage of the machine learning pipeline, from data intake to model evaluation. Let's get to it!
At the core of the
pyspark.ml module are the
Estimator classes. Almost every other class in the module behaves similarly to these two basic classes.
Transformer classes have a
.transform() method that takes a DataFrame and returns a new DataFrame; usually the original one with a new column appended. For example, you might use the class
Bucketizer to create discrete bins from a continuous feature or the class
PCA to reduce the dimensionality of your dataset using principal component analysis.
Estimator classes all implement a
.fit() method. These methods also take a DataFrame, but instead of returning another DataFrame they return a model object. This can be something like a
StringIndexerModel for including categorical data saved as strings in your models, or a
RandomForestModel that uses the random forest algorithm for classification or regression.
Which of the following is not true about machine learning in Spark?