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diff --git a/docs/ml-guide.md b/docs/ml-guide.md index de8fead352..01bf5ee18e 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -643,6 +643,13 @@ An important task in ML is *model selection*, or using data to find the best mod Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator). `CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. `CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`. + +The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator) +for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator) +for binary data or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator) +for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the setMetric +method in each of these evaluators. + The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model. `CrossValidator` finally fits the `Estimator` using the best `ParamMap` and the entire dataset. @@ -708,9 +715,12 @@ val pipeline = new Pipeline() // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. // This will allow us to jointly choose parameters for all Pipeline stages. // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. +// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric +// used is areaUnderROC. val crossval = new CrossValidator() .setEstimator(pipeline) .setEvaluator(new BinaryClassificationEvaluator) + // We use a ParamGridBuilder to construct a grid of parameters to search over. // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. @@ -831,9 +841,12 @@ Pipeline pipeline = new Pipeline() // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. // This will allow us to jointly choose parameters for all Pipeline stages. // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. +// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric +// used is areaUnderROC. CrossValidator crossval = new CrossValidator() .setEstimator(pipeline) .setEvaluator(new BinaryClassificationEvaluator()); + // We use a ParamGridBuilder to construct a grid of parameters to search over. // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. |