Spark ML Linear Regression - What Hyper-parameters to Tune - linear-regression

Question

I'm using the LinearRegression model in the Spark ML for predicting price. It is a single variate regression (x=time, y=price).
Assume my data is clean, what are the usual steps to take to improve this model?
So far, I tried tuning regularization parameter using cross-validation, and got rmse=15 given stdev=30.
Are there any other significant hyper-parameters I should care about? It seems Spark ML is not well documented for hyper-parameter tuning...
Update
I was able to tune up parameters using ParamGrid and Cross-Validation. However, is there any way to see how the fitted line looks like after correctly training a linear regression model? How can I know if the line is quadric or cubic etc? It would be great if there is a way to visualize the fitted line with all training data points.

Answer 1

The link you provided points to the main hyperparameters:
.setRegParam(0.3) // lambda for regularization
.setElasticNetParam(0.8) // coefficient for L1 vs L2
You can perform a GridSearch to optimize their usage .. say for
lambda in 0 to 0.8
elasticNet in 0 to 1.0
This can be done by providing ParamMap to CrossValidator
val estimatorParamMaps: Param[Array[ParamMap]]
param for estimator param maps

Answer 2

To answer your follow-up question, LinearRegression will also be a linear fit. You can plot it by predicting on a dataset of points across your range for your y-values with a line plot. Then, you can plot your training data on top of it.

Answer 3

val session = SparkSession.builder().master("local").appName("PredictiveAnalysis").getOrCreate();
def main(args: Array[String]): Unit = {
val data = session.sparkContext.textFile("C:\\Users\\Test\\new_workspace\\PredictionAlgo\\src\\main\\resources\\data.txt");
val parsedData = data.map { line =>
val x : Array[String] = line.replace(",", " ").split(" ")
val y = x.map{ (a => a.toDouble)}
val d = y.size - 1
val c = Vectors.dense(y(0),y(d))
LabeledPoint(y(0), c)
}.cache();
val numIterations = 100;
val stepSize = 0.00000001;
val model = LinearRegressionWithSGD.train(parsedData, numIterations, stepSize);
val valuesAndPreds = parsedData.map { point =>
val prediction = model.predict(point.features)
(point.label, prediction)
}
valuesAndPreds.foreach((result) => println(s"predicted label: ${result._1}, actual label: ${result._2}"))
val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2) }.mean()
println("training Mean Squared Error = " + MSE)
}
}