我想为我愚蠢的问题道歉，但我遇到了线性回归的问题。我在这方面遇到了很多困难。您能帮帮我吗？
这是我的主要代码。我目前使用一些外部库来绘制数据。
import com.fundtrml.config.ConfigSetUpimport org.apache.spark.ml.feature.LabeledPointimport org.apache.spark.ml.linalg.Vectorsimport org.apache.spark.ml.regression.LinearRegressionimport org.apache.spark.sql.SparkSessionobject SimpleLinearRegression { def main(args: Array[String]): Unit = { ConfigSetUp.HadoopBinariesConfig(); val ss = SparkSession.builder().appName("DataSet Test") .master("local[*]").getOrCreate() import ss.implicits._ var listOfData = List(40, 41, 45, 43, 42, 60, 61, 59, 50, 49, 47, 39, 41, 37, 36, 34, 33, 37) val data = listOfData //(1 to 21 by 1) // create a collection of Doubles .map(n => (n, n)) // make it pairs .map { case (label, features) => LabeledPoint(label, Vectors.dense(features)) } // create labeled points of dense vectors .toDF // make it a DataFrame var splittedData = data.randomSplit(Array(0.6,0.4)) var trainingData = splittedData(0) var testSetData = splittedData(1) trainingData.show() val lr = new LinearRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8) //train val model = lr.fit(trainingData) println(s"model.intercept: ${model.intercept}") println(s"model.coefficients : ${model.coefficients}") // Summarize the model over the training set and print out some metrics val trainingSummary = model.summary println(s"numIterations: ${trainingSummary.totalIterations}") println(s"objectiveHistory: [${trainingSummary.objectiveHistory.mkString(",")}]") trainingSummary.residuals.show() println(s"RMSE: ${trainingSummary.rootMeanSquaredError}") println(s"r2: ${trainingSummary.r2}") val predictions = model.transform(testSetData) predictions.show() //Display the data import com.quantifind.charts.Highcharts._ regression(listOfData) //using this external library with embeded functionality about regression var currentPredictions = predictions.select("prediction").rdd.map(r => r(0)).collect.toList println(currentPredictions)// regression(currentPredictions.map(_.toString.toDouble)) }}我的训练集如下，标签列 – 应预测的值，特征 – 用于进行预测的值：
+-----+--------+|label|features|+-----+--------+| 43.0| [43.0]|| 45.0| [45.0]|| 42.0| [42.0]|| 60.0| [60.0]|| 50.0| [50.0]|| 59.0| [59.0]|| 61.0| [61.0]|| 47.0| [47.0]|| 49.0| [49.0]|| 41.0| [41.0]|| 34.0| [34.0]|+-----+--------+评估回归模型时，我得到了以下数据：
model.intercept: 1.7363839862169372model.coefficients : [0.9640297102666925]numIterations: 3objectiveHistory: [0.5,0.406233822167566,0.031956224821402285]RMSE: 0.29784178261548705r2: 0.9987061382565019 --> 极高接近1最后，我得到了以下预测结果：
+-----+--------+------------------+|label|features| prediction|+-----+--------+------------------+| 40.0| [40.0]| 40.29757239688463|| 41.0| [41.0]|41.261602107151326|| 39.0| [39.0]|39.333542686617946|| 36.0| [36.0]|36.441453555817866|| 37.0| [37.0]| 37.40548326608456|| 33.0| [33.0]| 33.54936442501779|| 37.0| [37.0]| 37.40548326608456|+-----+--------+------------------+很容易看出预测结果并不在同一条线上。它们不可能位于直线上。这是使用Scala库WISP绘制的整个数据集
回答：
您绘制的内容似乎是Y轴上的标签和X轴上的列表索引，而不是X轴上的特征值。
当以特征对预测的方式绘制时，预测确实位于同一条线上。我这样做时得到的结果是：链接
