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Diffstat (limited to 'docs/mllib-linear-methods.md')
| -rw-r--r-- | docs/mllib-linear-methods.md | 18 |
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diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index e9b2d276cd..a3e1620c77 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -165,6 +165,8 @@ training algorithm on this training data using a static method in the algorithm object, and make predictions with the resulting model to compute the training error. +Refer to the [`SVMWithSGD` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.SVMWithSGD) and [`SVMModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.SVMModel) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD} import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics @@ -230,6 +232,8 @@ Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a calling `.rdd()` on your `JavaRDD` object. A self-contained application example that is equivalent to the provided example in Scala is given bellow: +Refer to the [`SVMWithSGD` Java docs](api/java/org/apache/spark/mllib/classification/SVMWithSGD.html) and [`SVMModel` Java docs](api/java/org/apache/spark/mllib/classification/SVMModel.html) for details on the API. + {% highlight java %} import scala.Tuple2; @@ -316,6 +320,8 @@ a dependency. The following example shows how to load a sample dataset, build SVM model, and make predictions with the resulting model to compute the training error. +Refer to the [`SVMWithSGD` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.SVMWithSGD) and [`SVMModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.SVMModel) for more details on the API. + {% highlight python %} from pyspark.mllib.classification import SVMWithSGD, SVMModel from pyspark.mllib.regression import LabeledPoint @@ -395,6 +401,8 @@ test, and use to fit a logistic regression model. Then the model is evaluated against the test dataset and saved to disk. +Refer to the [`LogisticRegressionWithLBFGS` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS) and [`LogisticRegressionModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionModel) for details on the API. + {% highlight scala %} import org.apache.spark.SparkContext import org.apache.spark.mllib.classification.{LogisticRegressionWithLBFGS, LogisticRegressionModel} @@ -441,6 +449,8 @@ test, and use to fit a logistic regression model. Then the model is evaluated against the test dataset and saved to disk. +Refer to the [`LogisticRegressionWithLBFGS` Java docs](api/java/org/apache/spark/mllib/classification/LogisticRegressionWithLBFGS.html) and [`LogisticRegressionModel` Java docs](api/java/org/apache/spark/mllib/classification/LogisticRegressionModel.html) for details on the API. + {% highlight java %} import scala.Tuple2; @@ -501,6 +511,8 @@ and make predictions with the resulting model to compute the training error. Note that the Python API does not yet support multiclass classification and model save/load but will in the future. +Refer to the [`LogisticRegressionWithLBFGS` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionWithLBFGS) and [`LogisticRegressionModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionModel) for more details on the API. + {% highlight python %} from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel from pyspark.mllib.regression import LabeledPoint @@ -558,6 +570,8 @@ The example then uses LinearRegressionWithSGD to build a simple linear model to values. We compute the mean squared error at the end to evaluate [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit). +Refer to the [`LinearRegressionWithSGD` Scala docs](api/scala/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD) and [`LinearRegressionModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.regression.LinearRegressionModel) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.regression.LinearRegressionModel @@ -600,6 +614,8 @@ Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a calling `.rdd()` on your `JavaRDD` object. The corresponding Java example to the Scala snippet provided, is presented bellow: +Refer to the [`LinearRegressionWithSGD` Java docs](api/java/org/apache/spark/mllib/regression/LinearRegressionWithSGD.html) and [`LinearRegressionModel` Java docs](api/java/org/apache/spark/mllib/regression/LinearRegressionModel.html) for details on the API. + {% highlight java %} import scala.Tuple2; @@ -673,6 +689,8 @@ values. We compute the mean squared error at the end to evaluate Note that the Python API does not yet support model save/load but will in the future. +Refer to the [`LinearRegressionWithSGD` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionWithSGD) and [`LinearRegressionModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel) for more details on the API. + {% highlight python %} from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel |