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Diffstat (limited to 'docs/mllib-isotonic-regression.md')
-rw-r--r-- | docs/mllib-isotonic-regression.md | 10 |
1 files changed, 9 insertions, 1 deletions
diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md index b521c2f27c..5732bc4c7e 100644 --- a/docs/mllib-isotonic-regression.md +++ b/docs/mllib-isotonic-regression.md @@ -60,7 +60,7 @@ Model is created using the training set and a mean squared error is calculated f labels and real labels in the test set. {% highlight scala %} -import org.apache.spark.mllib.regression.IsotonicRegression +import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel} val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt") @@ -88,6 +88,10 @@ val predictionAndLabel = test.map { point => // Calculate mean squared error between predicted and real labels. val meanSquaredError = predictionAndLabel.map{case(p, l) => math.pow((p - l), 2)}.mean() println("Mean Squared Error = " + meanSquaredError) + +// Save and load model +model.save(sc, "myModelPath") +val sameModel = IsotonicRegressionModel.load(sc, "myModelPath") {% endhighlight %} </div> @@ -150,6 +154,10 @@ Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map( ).rdd()).mean(); System.out.println("Mean Squared Error = " + meanSquaredError); + +// Save and load model +model.save(sc.sc(), "myModelPath"); +IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath"); {% endhighlight %} </div> </div> |