diff options
Diffstat (limited to 'python/pyspark/ml/regression.py')
| -rw-r--r-- | python/pyspark/ml/regression.py | 38 |
1 files changed, 18 insertions, 20 deletions
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 2803864ff4..ef77e19327 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -33,8 +33,7 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction Linear regression. The learning objective is to minimize the squared error, with regularization. - The specific squared error loss function used is: - L = 1/2n ||A weights - y||^2^ + The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^ This support multiple types of regularization: - none (a.k.a. ordinary least squares) @@ -191,7 +190,7 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance"): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance") """ super(DecisionTreeRegressor, self).__init__() @@ -213,9 +212,8 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi impurity="variance"): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, - maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, - impurity="variance") + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance") Sets params for the DecisionTreeRegressor. """ kwargs = self.setParams._input_kwargs @@ -286,10 +284,10 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", numTrees=20, featureSubsetStrategy="auto", seed=42): """ - __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, - maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", - numTrees=20, featureSubsetStrategy="auto", seed=42) + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ + impurity="variance", numTrees=20, featureSubsetStrategy="auto", seed=42) """ super(RandomForestRegressor, self).__init__() #: param for Criterion used for information gain calculation (case-insensitive). @@ -321,9 +319,9 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42, impurity="variance", numTrees=20, featureSubsetStrategy="auto"): """ - setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, - maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42, + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42, \ impurity="variance", numTrees=20, featureSubsetStrategy="auto") Sets params for linear regression. """ @@ -432,10 +430,10 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1): """ - __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, - maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared", - maxIter=20, stepSize=0.1) + __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ + lossType="squared", maxIter=20, stepSize=0.1) """ super(GBTRegressor, self).__init__() #: param for Loss function which GBT tries to minimize (case-insensitive). @@ -463,9 +461,9 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1): """ - setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, - maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, + setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ + maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ + maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ lossType="squared", maxIter=20, stepSize=0.1) Sets params for Gradient Boosted Tree Regression. """ |