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Diffstat (limited to 'python/pyspark/mllib/classification.py')
| -rw-r--r-- | python/pyspark/mllib/classification.py | 32 |
1 files changed, 16 insertions, 16 deletions
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index a765b1c4f7..cd43982191 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -79,15 +79,15 @@ class LogisticRegressionWithSGD(object): """ Train a logistic regression model on the given data. - @param data: The training data. - @param iterations: The number of iterations (default: 100). - @param step: The step parameter used in SGD + :param data: The training data. + :param iterations: The number of iterations (default: 100). + :param step: The step parameter used in SGD (default: 1.0). - @param miniBatchFraction: Fraction of data to be used for each SGD + :param miniBatchFraction: Fraction of data to be used for each SGD iteration. - @param initialWeights: The initial weights (default: None). - @param regParam: The regularizer parameter (default: 1.0). - @param regType: The type of regularizer used for training + :param initialWeights: The initial weights (default: None). + :param regParam: The regularizer parameter (default: 1.0). + :param regType: The type of regularizer used for training our model. :Allowed values: @@ -151,15 +151,15 @@ class SVMWithSGD(object): """ Train a support vector machine on the given data. - @param data: The training data. - @param iterations: The number of iterations (default: 100). - @param step: The step parameter used in SGD + :param data: The training data. + :param iterations: The number of iterations (default: 100). + :param step: The step parameter used in SGD (default: 1.0). - @param regParam: The regularizer parameter (default: 1.0). - @param miniBatchFraction: Fraction of data to be used for each SGD + :param regParam: The regularizer parameter (default: 1.0). + :param miniBatchFraction: Fraction of data to be used for each SGD iteration. - @param initialWeights: The initial weights (default: None). - @param regType: The type of regularizer used for training + :param initialWeights: The initial weights (default: None). + :param regType: The type of regularizer used for training our model. :Allowed values: @@ -238,10 +238,10 @@ class NaiveBayes(object): classification. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}). - @param data: RDD of NumPy vectors, one per element, where the first + :param data: RDD of NumPy vectors, one per element, where the first coordinate is the label and the rest is the feature vector (e.g. a count vector). - @param lambda_: The smoothing parameter + :param lambda_: The smoothing parameter """ sc = data.context jlist = sc._jvm.PythonMLLibAPI().trainNaiveBayes(data._to_java_object_rdd(), lambda_) |