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author | Holden Karau <holden@us.ibm.com> | 2016-05-24 22:20:00 -0700 |
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committer | Shivaram Venkataraman <shivaram@cs.berkeley.edu> | 2016-05-24 22:20:00 -0700 |
commit | cd9f16906cabd012b7676eb0f524e68a9cbe4db1 (patch) | |
tree | 89e07b90a3ffd87e4c6ff765a7694e11502ab153 /python/pyspark/ml | |
parent | c9c1c0e54d34773ac2cf5457fe5925559ece36c7 (diff) | |
download | spark-cd9f16906cabd012b7676eb0f524e68a9cbe4db1.tar.gz spark-cd9f16906cabd012b7676eb0f524e68a9cbe4db1.tar.bz2 spark-cd9f16906cabd012b7676eb0f524e68a9cbe4db1.zip |
[SPARK-15412][PYSPARK][SPARKR][DOCS] Improve linear isotonic regression pydoc & doc build insturctions
## What changes were proposed in this pull request?
PySpark: Add links to the predictors from the models in regression.py, improve linear and isotonic pydoc in minor ways.
User guide / R: Switch the installed package list to be enough to build the R docs on a "fresh" install on ubuntu and add sudo to match the rest of the commands.
User Guide: Add a note about using gem2.0 for systems with both 1.9 and 2.0 (e.g. some ubuntu but maybe more).
## How was this patch tested?
built pydocs locally, tested new user build instructions
Author: Holden Karau <holden@us.ibm.com>
Closes #13199 from holdenk/SPARK-15412-improve-linear-isotonic-regression-pydoc.
Diffstat (limited to 'python/pyspark/ml')
-rw-r--r-- | python/pyspark/ml/regression.py | 30 |
1 files changed, 17 insertions, 13 deletions
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 8f58594a66..1b7af7ef59 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -48,11 +48,15 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction The learning objective is to minimize the squared error, with regularization. The specific squared error loss function used is: L = 1/2n ||A coefficients - y||^2^ - This support multiple types of regularization: - - none (a.k.a. ordinary least squares) - - L2 (ridge regression) - - L1 (Lasso) - - L2 + L1 (elastic net) + This supports multiple types of regularization: + + * none (a.k.a. ordinary least squares) + + * L2 (ridge regression) + + * L1 (Lasso) + + * L2 + L1 (elastic net) >>> from pyspark.ml.linalg import Vectors >>> df = spark.createDataFrame([ @@ -128,7 +132,7 @@ class LinearRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): """ .. note:: Experimental - Model fitted by LinearRegression. + Model fitted by :class:`LinearRegression`. .. versionadded:: 1.4.0 """ @@ -503,13 +507,13 @@ class IsotonicRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): """ .. note:: Experimental - Model fitted by IsotonicRegression. + Model fitted by :class:`IsotonicRegression`. """ @property def boundaries(self): """ - Model boundaries. + Boundaries in increasing order for which predictions are known. """ return self._call_java("boundaries") @@ -769,7 +773,7 @@ class DecisionTreeRegressionModel(DecisionTreeModel, JavaMLWritable, JavaMLReada """ .. note:: Experimental - Model fitted by DecisionTreeRegressor. + Model fitted by :class:`DecisionTreeRegressor`. .. versionadded:: 1.4.0 """ @@ -887,7 +891,7 @@ class RandomForestRegressionModel(TreeEnsembleModels, JavaMLWritable, JavaMLRead """ .. note:: Experimental - Model fitted by RandomForestRegressor. + Model fitted by :class:`RandomForestRegressor`. .. versionadded:: 1.4.0 """ @@ -1021,7 +1025,7 @@ class GBTRegressionModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable): """ .. note:: Experimental - Model fitted by GBTRegressor. + Model fitted by :class:`GBTRegressor`. .. versionadded:: 1.4.0 """ @@ -1190,7 +1194,7 @@ class AFTSurvivalRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): """ .. note:: Experimental - Model fitted by AFTSurvivalRegression. + Model fitted by :class:`AFTSurvivalRegression`. .. versionadded:: 1.6.0 """ @@ -1380,7 +1384,7 @@ class GeneralizedLinearRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable """ .. note:: Experimental - Model fitted by GeneralizedLinearRegression. + Model fitted by :class:`GeneralizedLinearRegression`. .. versionadded:: 2.0.0 """ |