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Does PMML support probability calibration?
The Next CEO of Stack Overflow2019 Community Moderator ElectionProbability tree in SPSS/ R/ others?How to rank documents using Bag of words approachScala vs Java if you're NOT going to use Spark?Pruning and parameter reduction for decision treesFit Decision Tree to Gradient Boosted Trees for InterpretabilityDetecting and confirming sentiments in chat messagesArea Under Curve with probabilityAre there any good out-of-the-box language models for python?How to migrate R decision tree to JavaLightweight execution of Spark MLLib models
$begingroup$
As I need to port a decision tree model from Python to Java, I would like to know whether PMML (Predictive Model Markup Language) supports probability calibration.
python java
asked Mar 24 at 22:34
JavideJavide
1206
$endgroup$
add a comment |
$begingroup$
As I need to port a decision tree model from Python to Java, I would like to know whether PMML (Predictive Model Markup Language) supports probability calibration.
python java
asked Mar 24 at 22:34
JavideJavide
1206
$endgroup$
add a comment |
$begingroup$
As I need to port a decision tree model from Python to Java, I would like to know whether PMML (Predictive Model Markup Language) supports probability calibration.
python java
asked Mar 24 at 22:34
JavideJavide
1206
$endgroup$
As I need to port a decision tree model from Python to Java, I would like to know whether PMML (Predictive Model Markup Language) supports probability calibration.
python java
python java
asked Mar 24 at 22:34
JavideJavide
1206
asked Mar 24 at 22:34
JavideJavide
1206
asked Mar 24 at 22:34
JavideJavide
1206
asked Mar 24 at 22:34
JavideJavide
1206
asked Mar 24 at 22:34
JavideJavide
1206
1206
add a comment |
add a comment |
1 Answer
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$begingroup$
I'm not too familiar with PMML, but probability calibration (or at least, the most well-known methods, Platt scaling and isotonic regression) can be viewed as a stacked ensemble, with the output of your model being fed into a univariate regression model. PMML appears to support ensembling; see the fourth example at http://dmg.org/pmml/v4-3/MultipleModels.html
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
$endgroup$
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
I'm not too familiar with PMML, but probability calibration (or at least, the most well-known methods, Platt scaling and isotonic regression) can be viewed as a stacked ensemble, with the output of your model being fed into a univariate regression model. PMML appears to support ensembling; see the fourth example at http://dmg.org/pmml/v4-3/MultipleModels.html
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
$endgroup$
add a comment |
$begingroup$
I'm not too familiar with PMML, but probability calibration (or at least, the most well-known methods, Platt scaling and isotonic regression) can be viewed as a stacked ensemble, with the output of your model being fed into a univariate regression model. PMML appears to support ensembling; see the fourth example at http://dmg.org/pmml/v4-3/MultipleModels.html
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
$endgroup$
add a comment |
$begingroup$
I'm not too familiar with PMML, but probability calibration (or at least, the most well-known methods, Platt scaling and isotonic regression) can be viewed as a stacked ensemble, with the output of your model being fed into a univariate regression model. PMML appears to support ensembling; see the fourth example at http://dmg.org/pmml/v4-3/MultipleModels.html
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
$endgroup$
I'm not too familiar with PMML, but probability calibration (or at least, the most well-known methods, Platt scaling and isotonic regression) can be viewed as a stacked ensemble, with the output of your model being fed into a univariate regression model. PMML appears to support ensembling; see the fourth example at http://dmg.org/pmml/v4-3/MultipleModels.html
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
answered Mar 26 at 2:05
Ben ReinigerBen Reiniger
333210
333210
add a comment |
add a comment |
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