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author | Yanbo Liang <ybliang8@gmail.com> | 2016-01-05 14:24:32 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2016-01-05 14:24:32 -0800 |
commit | 1c6cf1a5639bf5111324e44d93a8c6462958750a (patch) | |
tree | 7f535a2afd24bb8c3822240b9158e034a303179d /docs/ml-classification-regression.md | |
parent | 6cfe341ee89baa952929e91d33b9ecbca73a3ea0 (diff) | |
download | spark-1c6cf1a5639bf5111324e44d93a8c6462958750a.tar.gz spark-1c6cf1a5639bf5111324e44d93a8c6462958750a.tar.bz2 spark-1c6cf1a5639bf5111324e44d93a8c6462958750a.zip |
[SPARK-12570][ML][DOC] DecisionTreeRegressor: provide variance of prediction: user guide update
Update user guide doc for ```DecisionTreeRegressor``` providing variance of prediction.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #10594 from yanboliang/spark-12570.
Diffstat (limited to 'docs/ml-classification-regression.md')
-rw-r--r-- | docs/ml-classification-regression.md | 11 |
1 files changed, 10 insertions, 1 deletions
diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md index d63438bf74..8ffc997b4b 100644 --- a/docs/ml-classification-regression.md +++ b/docs/ml-classification-regression.md @@ -535,7 +535,9 @@ The main differences between this API and the [original MLlib Decision Tree API] * use of DataFrame metadata to distinguish continuous and categorical features -The Pipelines API for Decision Trees offers a bit more functionality than the original API. In particular, for classification, users can get the predicted probability of each class (a.k.a. class conditional probabilities). +The Pipelines API for Decision Trees offers a bit more functionality than the original API. +In particular, for classification, users can get the predicted probability of each class (a.k.a. class conditional probabilities); +for regression, users can get the biased sample variance of prediction. Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described below in the [Tree ensembles section](#tree-ensembles). @@ -605,6 +607,13 @@ All output columns are optional; to exclude an output column, set its correspond <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td> <td>Classification only</td> </tr> + <tr> + <td>varianceCol</td> + <td>Double</td> + <td></td> + <td>The biased sample variance of prediction</td> + <td>Regression only</td> + </tr> </tbody> </table> |