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| author | Alexis Seigneurin <alexis.seigneurin@gmail.com> | 2015-09-19 12:01:22 +0100 |
|---|---|---|
| committer | Sean Owen <sowen@cloudera.com> | 2015-09-19 12:01:22 +0100 |
| commit | d83b6aae8b4357c56779cc98804eb350ab8af62d (patch) | |
| tree | bedbf25ea07ea27b9bae98cf7ac932fa99cb57be /docs/ml-guide.md | |
| parent | d507f9c0b7f7a524137a694ed6443747aaf90463 (diff) | |
| download | spark-d83b6aae8b4357c56779cc98804eb350ab8af62d.tar.gz spark-d83b6aae8b4357c56779cc98804eb350ab8af62d.tar.bz2 spark-d83b6aae8b4357c56779cc98804eb350ab8af62d.zip | |
Fixed links to the API
Submitting this change on the master branch as requested in https://github.com/apache/spark/pull/8819#issuecomment-141505941
Author: Alexis Seigneurin <alexis.seigneurin@gmail.com>
Closes #8838 from aseigneurin/patch-2.
Diffstat (limited to 'docs/ml-guide.md')
| -rw-r--r-- | docs/ml-guide.md | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/docs/ml-guide.md b/docs/ml-guide.md index c5d7f99002..0427ac6695 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -619,13 +619,13 @@ for row in selected.collect(): An important task in ML is *model selection*, or using data to find the best model or parameters for a given task. This is also called *tuning*. `Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately. -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). +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.evaluation.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) +The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator) +for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator) +for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.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. |