詳細は後ほど編集しなおします
とりあえずコードをこちらにうつす
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer}
import org.apache.spark.ml.feature.RFormula
import org.apache.spark.ml.evaluation.RegressionEvaluator

val filePath = "/home/ubuntu/LearningSparkV2/databricks-datasets/learning-spark-v2/sf-airbnb/sf-airbnb-clean.parquet/"
val airbnbDF = spark.read.parquet(filePath)
val Array(trainDF, testDF) = airbnbDF.randomSplit(Array(.8, .2), seed=42)

val vecAssembler = new VectorAssembler().setInputCols(Array("bedrooms", "bathrooms")).setOutputCol("features")
val lr = new LinearRegression().setFeaturesCol("features").setLabelCol("price")

val pipeline = new Pipeline().setStages(Array(vecAssembler, lr))
val pipelineModel = pipeline.fit(trainDF)
val predDF = pipelineModel.transform(testDF)


val categoricalCols = trainDF.dtypes.filter(_._2 == "StringType").map(_._1)
val indexOutputCols = categoricalCols.map(_ + "index")
val oheOutputCols = categoricalCols.map(_ + "OHE")

val stringIndexer = new StringIndexer().setInputCols(categoricalCols).setOutputCols(indexOutputCols).setHandleInvalid("skip")
val oheEncoder = new OneHotEncoder().setInputCols(categoricalCols).setOutputCols(oheOutputCols)
val numericCols = trainDF.dtypes.filter{ case (field, dataType) => dataType == "DoubleType" && field != "price"}.map(_._1)
val assemblerInputs = categoricalCols ++ numericCols
val vecAssembler = new VectorAssembler().setInputCols(assemblerInputs).setOutputCol("features")

val lr = new LinearRegression().setLabelCol("price").setFeaturesCol("features
")
val pipeline = new Pipeline().setStages(Array(stringIndexer, oheEncoder, vecAssembler, lr)
val pipelineModel = pipeline.fit(trainDF)
ここでなぜかエラー
ERROR Instrumentation: java.lang.IllegalArgumentException: requirement failed: Column host_is_superhost must be of type numeric but was actually of type string.
以下でも同じらしい(強制的にoheになるからそれでいい時だけっぽい、ただoheするなら以下が処理早いそう)

val rFormula = new RFormula().setFormula("price ~ .").setFeaturesCol("features").setLabelCol("price").setHandleInvalid("skip")
val pipeline = new Pipeline().setStages(Array(rFormula, lr))
val pipelineModel = pipeline.fit(trainDF)
val predDF = pipelineModel.transform(testDF)

val regressionEvaluator = new RegressionEvaluator().setPredictionCol("prediction").setLabelCol("price").setMetricName("rmse")
val rmse = regressionEvaluator.evaluate(predDF)