请写出机器学习分类的Scala命令

以下是使用Scala进行机器学习分类的命令：

1. 导入需要的库和算法模型

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorAssembler}
import org.apache.spark.ml.Pipeline

2. 加载数据集

val data = spark.read.format("csv")
    .option("header", "true")
    .option("inferSchema", "true")
    .load("path/to/dataset.csv")

3. 数据预处理

val labelIndexer = new StringIndexer()
  .setInputCol("label")
  .setOutputCol("indexedLabel")
  .fit(data)

val assembler = new VectorAssembler()
  .setInputCols(Array("feature1", "feature2", "feature3"))
  .setOutputCol("features")
  
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

4. 定义模型和评估器

val lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.01)

val labelConverter = new IndexToString()
  .setInputCol("prediction")
  .setOutputCol("predictedLabel")
  .setLabels(labelIndexer.labels)
  
val evaluator = new MulticlassClassificationEvaluator()
  .setLabelCol("indexedLabel")
  .setPredictionCol("prediction")
  .setMetricName("accuracy")

5. 构建管道并拟合模型

val pipeline = new Pipeline()
  .setStages(Array(labelIndexer, assembler, lr, labelConverter))

val model = pipeline.fit(trainingData)

6. 对测试数据进行预测并评估模型

val predictions = model.transform(testData)

val accuracy = evaluator.evaluate(predictions)

println("Test Error = " + (1.0 - accuracy))