Hypergbm

Latest version: v0.3.2

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0.3.2

Add compatibility with scikit-learn v1.4.x
Update experiment discriminator option to disable or enable it
Make imbalanced-learn optional

0.3.1

* Add compatibility with lightgbm v4.x
* Fix bug: failed to pickle estimator on notebook, see: https://github.com/DataCanvasIO/HyperGBM/issues/104.

0.3.0

We add a few new features to this version:

* Multi-objectives optimization

* optimization algorithm
- add MOEA/D(Multiobjective Evolutionary Algorithm Based on Decomposition)
- add Tchebycheff, Weighted Sum, Penalty-based boundary intersection approach(PBI) decompose approachs
- add shuffle crossover, uniform crossover, single point crossover strategies for GA based algorithms
- automatically normalize objectives of different dimensions
- automatically convert maximization problem to minimization problem
- add NSGA-II(Non-dominated Sorting Genetic Algorithm)
- add R-NSGA-II(A new dominance relation for multicriteria decision making)

* builtin objectives
- number of features
- prediction performance

0.2.5.7

* Add experiment option `n_jobs`
* Upgrade hboard to v0.1.1

0.2.5.6

* Fix bug: EarlyStopping does not work.

0.2.5.5

* Add compatibility with xgboost v1.6
* Add compatibility with cuML 22.08
* Add a shap explainer: `HyperGBMShapExplainer`
* Make python-geohash ( which requires native c compiler ) optional

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