Comments (4)
I have also missed something like that in Julia. But perhaps it would fit better to OnlineStats.jl?
from decisiontree.jl.
Agreed, although my feeling is that the types and methods available in this package (DT) might be needed for a VFDT implementation and, in some sense, it is not as clear that a VFDT has O(1) memory requirement (since the tree might grow very large).
from decisiontree.jl.
What is your opinion on this, @joshday? Would VFDT fit the scope of OnlineStats.jl or would it better fit elsewhere?
from decisiontree.jl.
Yes, it would fit in the scope of OnlineStats. I actually started working on it recently, but @robertfeldt makes a good point that before it's done I'll probably be reinventing some data structures that exist here. I think that's fine at least in the short term.
from decisiontree.jl.
Related Issues (20)
- Citation / Reference for DecisionTree.jl HOT 12
- RNG “shuffling” introduced in #174 is fundamentally flawed HOT 17
- Round thresholds in display of trees HOT 2
- Compatibility bounds for AbstractTrees.jl HOT 1
- Replicate Python model in Julia HOT 4
- Feature importance from random forest regression HOT 5
- Add multithreading support in RF predictions: probabilistic classification - and regression HOT 2
- Custom stopping criteria and loss functions HOT 9
- Fail to precompile the DecisionTree.jl on M1 mac HOT 2
- Add functionality for adding trees to an existing forest HOT 2
- [Tracking Issue] Add document strings to public methods
- Add support for specifying the `loss` used in random forests and AdaBoost model HOT 4
- Standardize the way fit! and predict methods take X matrix (features) HOT 3
- precompile problem julia 1.8.5 LinuxMint HOT 1
- Is out-of-bag error of RandomForestClassifier implementable ? HOT 2
- documentation: Clarify n_subfeatures in build_tree? HOT 4
- Why regression used in apply_forest only if type of labels in model is Float64? HOT 2
- Can offer a interface for DataFrames.jl? HOT 2
- Feature Request: Class Weighting capabilities HOT 1
- Memory leakage upon repeated training HOT 1
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from decisiontree.jl.