Github user pwendell commented on the pull request:
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@manishamde - can you add `[MLlib]` to the title of this pull request?
Otherwise it doesn't get filtered properly by our filters.
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Github user manishamde commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -768,104 +973,157 @@ object DecisionTree extends Serializable with
Github user manishamde commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-48867199
Thanks Evan. I have compared to scikit-learn on the covertype dataset and
the results looked similar.
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Github user etrain commented on a diff in the pull request:
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--- Diff:
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@@ -768,104 +973,157 @@ object DecisionTree extends Serializable with
Github user etrain commented on the pull request:
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I've gone through this in some depth, and aside from a couple of minor
style nits - the logic looks good to me. Manish - have you compared output vs.
scikit-learn for
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Github user manishamde commented on the pull request:
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@etrain Added implicit conversion. :-)
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Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/886#discussion_r13982468
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
---
@@ -49,6 +49,7 @@ object DecisionTreeRunner {
case
Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/886#discussion_r13982568
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -45,7 +46,7 @@ class DecisionTree (private val strategy: Strategy)
Github user manishamde commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -233,13 +234,73 @@ object DecisionTree extends Serializable with
Github user manishamde commented on the pull request:
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Thanks @etrain
1. I will try to use implicits
2. I agree. We originally had separate trees and then merged them for
readability. There is a sweet spot in
Github user etrain commented on a diff in the pull request:
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
---
@@ -49,6 +49,7 @@ object DecisionTreeRunner {
case class
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--- Diff:
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---
@@ -49,6 +49,7 @@ object DecisionTreeRunner {
case
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--- Diff:
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@@ -49,6 +49,7 @@ object DecisionTreeRunner {
case
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
---
@@ -49,6 +49,7 @@ object DecisionTreeRunner {
case class
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -45,7 +46,7 @@ class DecisionTree (private val strategy: Strategy)
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -212,7 +211,9 @@ object DecisionTree extends Serializable with Logging {
Github user etrain commented on a diff in the pull request:
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--- Diff:
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Github user etrain commented on the pull request:
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I've taken a first pass at this and at a high level it looks good.
The main two things I'd say are
1) I think an implicit that converts LabeledPoint to
Github user manishamde commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-45959540
Friendly nudge: could somebody please take a look at this PR. It is
blocking upcoming ensemble tree PRs.
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Github user manishamde commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-45141375
I added support for sorting categorical feature values using impurity
(gini/entropy) calculated over the corresponding labels in multiclass
classification. This
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-44381347
I don't have a reference, and I did look for one. I am sure it is not
optimal, and not even that great as a greedy algorithm. Two low-entropy
distributions over target
Github user manishamde commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-44388751
I fully agree.
I will give others a day or two to raise any concerns if they have any and
then proceed to implement the two-step solution for multiclass
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-44243946
@manishamde Yes for categorical features with high cardinality, you don't
want to consider all possible splits. I don't think having a cardinality of 30
or 40 is that
Github user manishamde commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-44359841
@srowen It's good to know about the use-case for cardinality in the order
of tens.
The categorical feature ordering using the average value of the target
Github user etrain commented on the pull request:
https://github.com/apache/spark/pull/886#issuecomment-44360446
I am worried that exponential growth in the number of split possibilities
kills us when we check for all splits when we get to even 20-30
categorical values. That's
GitHub user manishamde opened a pull request:
https://github.com/apache/spark/pull/886
SPARK-1536: multiclass classification support for decision tree
The ability to perform multiclass classification is a big advantage for
using decision trees and was a highly requested feature for
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