diff options
| -rw-r--r-- | python/pyspark/ml/evaluation.py | 13 |
1 files changed, 12 insertions, 1 deletions
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py index 2a41678741..719c0c7d79 100644 --- a/python/pyspark/ml/evaluation.py +++ b/python/pyspark/ml/evaluation.py @@ -105,6 +105,8 @@ class JavaEvaluator(JavaParams, Evaluator): @inherit_doc class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol): """ + .. note:: Experimental + Evaluator for binary classification, which expects two input columns: rawPrediction and label. The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities). @@ -172,6 +174,8 @@ class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPrediction @inherit_doc class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol): """ + .. note:: Experimental + Evaluator for Regression, which expects two input columns: prediction and label. @@ -193,7 +197,11 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol): # when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`), # we take and output the negative of this metric. metricName = Param(Params._dummy(), "metricName", - "metric name in evaluation (mse|rmse|r2|mae)", + """metric name in evaluation - one of: + rmse - root mean squared error (default) + mse - mean squared error + r2 - r^2 metric + mae - mean absolute error.""", typeConverter=TypeConverters.toString) @keyword_only @@ -241,8 +249,11 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol): @inherit_doc class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol): """ + .. note:: Experimental + Evaluator for Multiclass Classification, which expects two input columns: prediction and label. + >>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0), ... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)] >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["prediction", "label"]) |