Release 0.3.2

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

Release 0.3.1

• Add compatibility with lightgbm v4.x
• Fix bug: failed to pickle estimator on notebook, see: #104.

Release 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

Release 0.2.5.7

• Add experiment option n_jobs
• Upgrade hboard to v0.1.1

Release 0.2.5.6

• Fix bug: EarlyStopping does not work.

Release 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

Release 0.2.5.4

• Add compatibility with scikit-learn v1.1
• Fix TfidfPrimitive

Release 0.2.5.3

• Support custom metric in experiment visualization
• Set experiment cv default to False if eval_data is not None
• Fix issues: #80, #82

Release 0.2.5.2

• Fix experiment report in gpu mode

Upgrade to 0.2.5.1

• Add compatibility with cuML 22.02