Contrasted with linear regression where the output is assumed to follow a Gaussian distribution, generalized linear models (GLMs) are specifications of linear models where the response variable Yi follows some distribution from the exponential family of distributions. Spark’s GeneralizedLinearRegression interface allows for flexible specification of GLMs which can be used for various types of prediction problems including linear regression, Poisson regression, logistic regression, and others. Currently in spark.ml, only a subset of the exponential family distributions are supported and they are listed below.
NOTE: Spark currently only supports up to 4096 features through its GeneralizedLinearRegression interface, and will throw an exception if this constraint is exceeded.
Spark’s generalized linear regression interface also provides summary statistics for diagnosing the fit of GLM models, including residuals, p-values, deviances, the Akaike information criterion, and others.
import org.apache.spark.ml.regression.GeneralizedLinearRegression
// Load training data
val dataset = spark.read.format("libsvm")
.load("data/mllib/sample_linear_regression_data.txt")
val glr = new GeneralizedLinearRegression()
.setFamily("gaussian")
.setLink("identity")
.setMaxIter(10)
.setRegParam(0.3)
// Fit the model
val model = glr.fit(dataset)
// Print the coefficients and intercept for generalized linear regression model
println(s"Coefficients: ${model.coefficients}")
println(s"Intercept: ${model.intercept}")
// Summarize the model over the training set and print out some metrics
val summary = model.summary
println(s"Coefficient Standard Errors: ${summary.coefficientStandardErrors.mkString(",")}")
println(s"T Values: ${summary.tValues.mkString(",")}")
println(s"P Values: ${summary.pValues.mkString(",")}")
println(s"Dispersion: ${summary.dispersion}")
println(s"Null Deviance: ${summary.nullDeviance}")
println(s"Residual Degree Of Freedom Null: ${summary.residualDegreeOfFreedomNull}")
println(s"Deviance: ${summary.deviance}")
println(s"Residual Degree Of Freedom: ${summary.residualDegreeOfFreedom}")
println(s"AIC: ${summary.aic}")
println("Deviance Residuals: ")
summary.residuals().show()
/*
Output:
Coefficients: [0.010541828081257216,0.8003253100560949,-0.7845165541420371,2.3679887171421914,0.5010002089857577,1.1222351159753026,-0.2926824398623296,-0.49837174323213035,-0.6035797180675657,0.6725550067187461]
Intercept: 0.14592176145232041
Coefficient Standard Errors: 0.7950428434287478,0.8049713176546897,0.7975916824772489,0.8312649247659919,0.7945436200517938,0.8118992572197593,0.7919506385542777,0.7973378214726764,0.8300714999626418,0.7771333489686802,0.463930109648428
T Values: 0.013259446542269243,0.9942283563442594,-0.9836067393599172,2.848657084633759,0.6305509179635714,1.382234441029355,-0.3695715687490668,-0.6250446546128238,-0.7271418403049983,0.8654306337661122,0.31453393176593286
P Values: 0.989426199114056,0.32060241580811044,0.3257943227369877,0.004575078538306521,0.5286281628105467,0.16752945248679119,0.7118614002322872,0.5322327097421431,0.467486325282384,0.3872259825794293,0.753249430501097
Dispersion: 105.60988356821714
Null Deviance: 53229.3654338832
Residual Degree Of Freedom Null: 500
Deviance: 51748.8429484264
Residual Degree Of Freedom: 490
AIC: 3769.1895871765314
Deviance Residuals:
+-------------------+
| devianceResiduals|
+-------------------+
|-10.974359174246889|
| 0.8872320138420559|
| -4.596541837478908|
|-20.411667435019638|
|-10.270419345342642|
|-6.0156058956799905|
|-10.663939415849267|
| 2.1153960525024713|
| 3.9807132379137675|
|-17.225218272069533|
| -4.611647633532147|
| 6.4176669407698546|
| 11.407137945300537|
| -20.70176540467664|
| -2.683748540510967|
|-16.755494794232536|
| 8.154668342638725|
|-1.4355057987358848|
|-0.6435058688185704|
| -1.13802589316832|
+-------------------+
only showing top 20 rows
*/
