Re: Does DecisionTree model in MLlib deal with missing values?
On Sun, Jan 11, 2015 at 9:46 PM, Christopher Thom christopher.t...@quantium.com.au wrote: Is there any plan to extend the data types that would be accepted by the Tree models in Spark? e.g. Many models that we build contain a large number of string-based categorical factors. Currently the only strategy is to map these string values to integers, and store the mapping so the data can be remapped when the model is scored. A viable solution, but cumbersome for models with hundreds of these kinds of factors. I think there is nothing on the roadmap, except that in the newer ML API (the bits under spark.ml), there's fuller support for the idea of a pipeline of transformations, of which performing this encoding could be one step. Since it's directly relevant, I don't mind mentioning that we did build this sort of logic on top of MLlib and PMML. There's nothing hard about it, just a pile of translation and counting code, such as in https://github.com/OryxProject/oryx/blob/master/oryx-app-common/src/main/java/com/cloudera/oryx/app/rdf/RDFPMMLUtils.java So there are bits you can reuse out there especially if your goal is to get to PMML, which will want to represent all the actual categorical values in its DataDictionary and not encodings. Concerning missing data, I haven't been able to figure out how to use NULL values in LabeledPoints, and I'm not sure whether DecisionTrees correctly handle the case of missing data. The only thing I've been able to work out is to use a placeholder value, Yes, I don't think that's supported. In model training, you can simply ignore data that can't reach the node because it lacks a feature needed in a decision rule. This is OK as long as not that much data is missing. In scoring you can't not-answer. Again if you refer to PMML, you can see some ideas about how to handle this: http://www.dmg.org/v4-2-1/TreeModel.html#xsdType_MISSING-VALUE-STRATEGY - Make no prediction - Just copy the last prediction - Use a model-supplied default for the node - Use some confidence weighted combination of the answer you'd get by following both paths I have opted, in the past, for simply defaulting to the subtree with more training examples. All of these strategies are approximations, yes. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Does DecisionTree model in MLlib deal with missing values?
I do not recall seeing support for missing values. Categorical values are encoded as 0.0, 1.0, 2.0, ... When training the model you indicate which are interpreted as categorical with the categoricalFeaturesInfo parameter, which maps feature offset to count of distinct categorical values for the feature. On Sun, Jan 11, 2015 at 6:54 AM, Carter gyz...@hotmail.com wrote: Hi, I am new to the MLlib in Spark. Can the DecisionTree model in MLlib deal with missing values? If so, what data structure should I use for the input? Moreover, my data has categorical features, but the LabeledPoint requires double data type, in this case what can I do? Thank you very much. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Does-DecisionTree-model-in-MLlib-deal-with-missing-values-tp21080.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
RE: Does DecisionTree model in MLlib deal with missing values?
Is there any plan to extend the data types that would be accepted by the Tree models in Spark? e.g. Many models that we build contain a large number of string-based categorical factors. Currently the only strategy is to map these string values to integers, and store the mapping so the data can be remapped when the model is scored. A viable solution, but cumbersome for models with hundreds of these kinds of factors. Concerning missing data, I haven't been able to figure out how to use NULL values in LabeledPoints, and I'm not sure whether DecisionTrees correctly handle the case of missing data. The only thing I've been able to work out is to use a placeholder value, which is not really what is needed. I think this will introduce bias in the model if there is a significant proportion of missing data. e.g. suppose we have a factor that is TimeSpentonX. If 20% of values are missing, what numeric value should this missing data be replaced with? Almost every choice will bias the final model...what we really want is the algorithm to just ignore those values. cheers chris -Original Message- From: Sean Owen [mailto:so...@cloudera.com] Sent: Sunday, 11 January 2015 10:53 PM To: Carter Cc: user@spark.apache.org Subject: Re: Does DecisionTree model in MLlib deal with missing values? I do not recall seeing support for missing values. Categorical values are encoded as 0.0, 1.0, 2.0, ... When training the model you indicate which are interpreted as categorical with the categoricalFeaturesInfo parameter, which maps feature offset to count of distinct categorical values for the feature. On Sun, Jan 11, 2015 at 6:54 AM, Carter gyz...@hotmail.com wrote: Hi, I am new to the MLlib in Spark. Can the DecisionTree model in MLlib deal with missing values? If so, what data structure should I use for the input? Moreover, my data has categorical features, but the LabeledPoint requires double data type, in this case what can I do? Thank you very much. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Does-DecisionTree- model-in-MLlib-deal-with-missing-values-tp21080.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org Christopher Thom QUANTIUM Level 25, 8 Chifley, 8-12 Chifley Square Sydney NSW 2000 T: +61 2 8222 3577 F: +61 2 9292 6444 W: quantium.com.auwww.quantium.com.au linkedin.com/company/quantiumwww.linkedin.com/company/quantium facebook.com/QuantiumAustraliawww.facebook.com/QuantiumAustralia twitter.com/QuantiumAUwww.twitter.com/QuantiumAU The contents of this email, including attachments, may be confidential information. If you are not the intended recipient, any use, disclosure or copying of the information is unauthorised. If you have received this email in error, we would be grateful if you would notify us immediately by email reply, phone (+ 61 2 9292 6400) or fax (+ 61 2 9292 6444) and delete the message from your system. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Does DecisionTree model in MLlib deal with missing values?
Hi, I am new to the MLlib in Spark. Can the DecisionTree model in MLlib deal with missing values? If so, what data structure should I use for the input? Moreover, my data has categorical features, but the LabeledPoint requires double data type, in this case what can I do? Thank you very much. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Does-DecisionTree-model-in-MLlib-deal-with-missing-values-tp21080.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
