In Spark ML, the linear regression model expects the feature column to be a vector type. However, if thefeaturescolumn in the DataFrametrain_dfis not already in this format (such as being a column of type UDT or a non-vectorized type), the engineerneeds to convert it to a vector column using a transformer likeVectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.

References

Spark MLlib documentation forLinearRegression:https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression