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https://issues.apache.org/jira/browse/HIVEMALL-245?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16900743#comment-16900743
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ASF GitHub Bot commented on HIVEMALL-245:
-----------------------------------------
myui commented on issue #198: [WIP][HIVEMALL-245] Refactor RandomForest for
Sparse Data handling
URL:
https://github.com/apache/incubator-hivemall/pull/198#issuecomment-518488243
TODO
- [x] prune redundant branches
- [x] reduce memory usage by using RoaringBitmap for AttributeType
- [ ] support prediction tracing
https://issues.apache.org/jira/browse/HIVEMALL-171
- [x] refactor RegressionTree as well
- [ ] introduce more [sophisticated post
pruning](https://en.wikipedia.org/wiki/Decision_tree_pruning#Reduced_error_pruning)
- [ ] support the default value for missing values
- [ ] Fix split handling of sparse numeric values
- problem: split does not occur when column values have a single value
- if(x<=1.0) { .. } else { ... } never split where sparse x is already
1.0
- if there are only a single value for a column, then treat it as nominal
value (?)
- [ ] use more robust RNG in [feature
sampling](https://github.com/apache/incubator-hivemall/blob/master/core/src/main/java/hivemall/smile/classification/DecisionTree.java#L636)
```
Reservoir Sampling returns not-well shuffled result for small stream but
it’s accepted in Best splitter (?) => more randomness might be required
(replace java.util.Random ?)
```
- [ ] optimize
[split](https://github.com/scikit-learn/scikit-learn/blob/4de404d46d24805ff48ad255ec3169a5155986f0/sklearn/tree/_tree.pyx#L224)
```
min_sample_leaf >= 2 is satisfied iff min_sample_split >= 4
So, split only happens when samples in intermediate nodes has >= 2 *
min_sample_leaf nodes.
min_sample_leaf = 2 replaces min_sample_split = 3 with min_sample_split = 4
```
- [ ] memoize [a redundant split
computation](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_splitter.pyx#L1241)
```
Skip the CPU intensive evaluation of the impurity criterion for features
that were already detected as constant (hence not suitable for good splitting)
by ancestor nodes and save the information on newly discovered constant
features to spare computation on descendant nodes.
```
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> Refactor RandomForest for Sparse Data handling
> ----------------------------------------------
>
> Key: HIVEMALL-245
> URL: https://issues.apache.org/jira/browse/HIVEMALL-245
> Project: Hivemall
> Issue Type: Improvement
> Affects Versions: 0.5.2
> Reporter: Makoto Yui
> Assignee: Makoto Yui
> Priority: Major
> Fix For: 0.6.0
>
>
> * Fix attribute to use RoaringBitmap instead of AttributeType[]
> * Support pruning of redundant decision tree nodes
> * Support the default value for missing values
> * Fix split handling of sparse numeric values
> ** problem: split does not occur when column values have a single value
> *** if(x<=1.0) \{ .. } else \{ ... } never split where sparse x is already
> 1.0
> ** if there are only a single value for a column, then treat it as nominal
> value (?)
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