Scala:如何修改交叉验证的默认指标

import org.apache.spark.ml.evaluation.{Evaluator, RegressionEvaluator}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tuning.{CrossValidator, CrossValidatorModel, ParamGridBuilder}
import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel}
import org.apache.spark.sql.{DataFrame, SparkSession}

// 创建SparkSession
val spark = SparkSession.builder()
  .appName("CrossValidationExample")
  .getOrCreate()

// 加载数据集
val data: DataFrame = spark.read.format("libsvm").load("data/mllib/sample_linear_regression_data.txt")

// 创建线性回归模型
val lr = new LinearRegression()

// 创建自定义评估器
val maeEvaluator = new RegressionEvaluator()
  .setLabelCol(lr.getLabelCol)
  .setPredictionCol(lr.getPredictionCol)
  .setMetricName("mae")

// 创建参数网格
val paramGrid = new ParamGridBuilder()
  .addGrid(lr.regParam, Array(0.1, 0.01))
  .addGrid(lr.fitIntercept)
  .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
  .build()

// 创建交叉验证器
val cv = new CrossValidator()
  .setEstimator(lr)
  .setEvaluator(maeEvaluator)
  .setEstimatorParamMaps(paramGrid)
  .setNumFolds(3)

// 运行交叉验证
val cvModel: CrossValidatorModel = cv.fit(data)

// 获取最佳模型
val bestModel: LinearRegressionModel = cvModel.bestModel.asInstanceOf[LinearRegressionModel]

// 打印最佳模型的参数
println(s"Best model params: regParam = ${bestModel.getRegParam}, elasticNetParam = ${bestModel.getElasticNetParam}")

// 关闭SparkSession
spark.stop()