random Trees - RDD-based API scala语言代码
时间: 2024-01-07 20:05:47 浏览: 31
好的,如果你想使用 Spark MLlib 中的随机树算法(Random Trees),可以按照以下步骤来实现:
1. 导入相关的类和包
```scala
import org.apache.spark.mllib.tree.RandomTree
import org.apache.spark.mllib.tree.configuration.Strategy
import org.apache.spark.mllib.tree.model.RandomTreeModel
import org.apache.spark.mllib.util.MLUtils
```
2. 加载训练数据,并将其转换为 RDD
```scala
val data = MLUtils.loadLibSVMFile(sc, "<path_to_train_data>")
```
3. 定义算法的参数和配置
```scala
val numClasses = 2
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 10
val featureSubsetStrategy = "auto"
val impurity = "gini"
val maxDepth = 5
val maxBins = 32
val seed = 12345
val strategy = Strategy.defaultStrategy("Classification")
.setNumClasses(numClasses)
.setCategoricalFeaturesInfo(categoricalFeaturesInfo)
.setMaxDepth(maxDepth)
.setMaxBins(maxBins)
.setSubsamplingRate(1.0)
```
4. 训练模型
```scala
val model = RandomTree.train(data, strategy, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed)
```
5. 保存模型
```scala
model.save(sc, "<path_to_save_model>")
```
其中,`<path_to_train_data>` 为训练数据的路径,`<path_to_save_model>` 为保存模型的路径。
希望这个回答能够帮到你!