serengil / chefboost
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
☆471Updated 5 months ago
Alternatives and similar repositories for chefboost:
Users that are interested in chefboost are comparing it to the libraries listed below
- A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.☆468Updated last year
- A Python library for dynamic classifier and ensemble selection☆484Updated 11 months ago
- A python implementation of C4.5 algorithm by R. Quinlan☆110Updated 3 years ago
- Genetic feature selection module for scikit-learn☆324Updated last year
- scikit-learn contrib estimators☆194Updated this week
- A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.☆574Updated 9 months ago
- Leave One Feature Out Importance☆830Updated last month
- Python package for Imputation Methods☆248Updated last year
- Fast SHAP value computation for interpreting tree-based models☆537Updated last year
- A machine learning package for streaming data in Python. The other ancestor of River.☆773Updated last year
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).☆1,443Updated 3 weeks ago
- Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ra…☆750Updated 7 months ago
- A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.☆416Updated 2 years ago
- Python Accumulated Local Effects package☆165Updated 2 years ago
- A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.☆612Updated last year
- Extra blocks for scikit-learn pipelines.☆1,314Updated 2 weeks ago
- machine learning with logical rules in Python☆633Updated last year
- Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshad…☆631Updated last month
- Python 3 wrapper for Weka using JPype.☆146Updated 3 months ago
- Frouros: an open-source Python library for drift detection in machine learning systems.☆212Updated 2 months ago
- Linear Prediction Model with Automated Feature Engineering and Selection Capabilities☆512Updated last week
- A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection fea…☆654Updated last year
- Model Error Analysis for scikit-learn models.☆29Updated 3 years ago
- Two algorithms based on linear programming to discover classification rules for interpretable learning.☆22Updated 4 months ago
- Multiple Imputation with LightGBM in Python☆374Updated 8 months ago
- zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range fr…☆244Updated 4 months ago
- Forecasting with Gradient Boosted Time Series Decomposition☆195Updated last year
- Python implementations of the Boruta all-relevant feature selection method.☆1,560Updated 7 months ago
- An extension of XGBoost to probabilistic modelling☆592Updated 8 months ago
- EvalML is an AutoML library written in python.☆805Updated last week