Comments (1)
This turned out to be an interesting fix because it gave me the opportunity to rethink the way that node splitting is done in our base k-d tree implementation. It turns out we can save alot of computation and still get fat splits (tight bounds) if we pick the axis as the column with the longest range (instead of highest variance) and the value as the midrange (instead of median).
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Related Issues (20)
- Prune redundant Decision Tree leaf nodes
- Fixed Array Memory Optimizations HOT 2
- Use new PHP 8.0 features in version 3.0
- Warning: Ambiguous class resolution, `Rubix\ML\Kernels\Distance\Gower` HOT 4
- Use pretrained models? HOT 3
- How to Train only One Class? HOT 4
- Not working in PHP 8.2 because of voku/portable-utf8/src/voku/helper/UTF8.php HOT 7
- Question, which model users for Fraud Prediction HOT 2
- Incorrect SVC save/load methods implementation HOT 2
- psr/log old version is limiting the project to be used on modern frameworks HOT 2
- Which alogorithm can be used for search result ranking ? HOT 2
- Is "Transformer Architecture Marchine Learning Model" supported on RubixML ??? HOT 4
- Map method in Dataset doesn't exist HOT 2
- Multi Language Tokenization Support HOT 2
- WordCountVectorizer Memory Issue HOT 2
- TruncatedSVD() made PHP crash without any message HOT 3
- Evaluation of the cluster quality with indicators HOT 1
- Requirements not resolved to an installable set of packages HOT 3
- Softmax Classifier & partial training HOT 1
- Does Rubix ML support Natural Language Processing (NLP)? HOT 1
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