In the context of feature engineering using Spark ML, explain the process of handling categorical features and encoding them for use in machine learning models. Provide a code snippet demonstrating the encoding of categorical features using Spark ML transformers.

Simulated

Use the StringIndexer transformer from the pyspark.ml.feature module to convert categorical features to numerical indices.

17.9%

Use the OneHotEncoder transformer from the pyspark.ml.feature module to convert categorical features to a binary vector representation.

57.1%

Use the VectorAssembler transformer from the pyspark.ml.feature module to combine categorical features into a single vector column.

17.9%

Use the Bucketizer transformer from the pyspark.ml.feature module to map categorical features to a fixed-size vector of values.

7.1%

Databricks Certified Machine Learning - Associate