Alternatives and similar repositories for Rule_Extraction_from_Trees

Users that are interested in Rule_Extraction_from_Trees are comparing it to the libraries listed below

• jovicigor / DecisionTreeRuleExtraction
  This project is a research on how to extract rules from the existing data using trained Decision Tree. The dataset used to train the mode…
  ☆16Updated 6 years ago
• jrzaurin / LightGBM-with-Focal-Loss
  An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems
  ☆256Updated 6 years ago
• jia-zhuang / xgboost-lightgbm-hyperparameter-tuning
  Bayesian Optimization and Grid Search for xgboost/lightgbm
  ☆79Updated 11 months ago
• finafily526 / A_Model_of_Risk_Control
  关于评分卡的基本流程
  ☆68Updated 6 years ago
• 360digitech / GBST
  GBST is an optimized distributed gradient boosting survival trees library that is implemented based on the XGBoost
  ☆37Updated 5 years ago
• jhwjhw0123 / Imbalance-XGBoost
  XGBoost for label-imbalanced data: XGBoost with weighted and focal loss functions
  ☆337Updated last year
• antmachineintelligence / mtgbmcode
  mtgbmcode
  ☆178Updated 3 years ago
• LenzDu / Kaggle-Competition-Favorita
  5th place solution for Kaggle competition Favorita Grocery Sales Forecasting
  ☆261Updated 7 years ago
• taenggu0309 / Semi-auto-modeling
  ☆37Updated 5 years ago
• boredbird / woe
  Tools for WoE Transformation mostly used in ScoreCard Model for credit rating
  ☆256Updated 6 years ago
• Lantianzz / Scorecard-Bundle
  A High-level Scorecard Modeling API | 评分卡建模尽在于此
  ☆82Updated 3 years ago
• kaiwang0112006 / autoBinning
  分箱工具
  ☆20Updated 5 years ago
• duxuhao / Feature-Selection
  Features selector based on the self selected-algorithm, loss function and validation method
  ☆680Updated 6 years ago
• rogeroyer / feature_selection_GAAlgorithm
  基于遗传算法的特征选择
  ☆127Updated 6 years ago
• ZGLee96 / gbdt_var
  根据GBDT衍生变量，并对衍生后的变量进行应用
  ☆21Updated 5 years ago
• YC-Coder-Chen / feature-engineering-handbook
  A practical feature engineering handbook
  ☆332Updated 5 years ago
• gkapatai / MaatPy
  ☆15Updated 2 years ago
• weepon / feature_selection
  常用的特征选择方法
  ☆67Updated 3 years ago
• msahamed / handle_imabalnce_class
  Address imbalance classes in machine learning projects.
  ☆65Updated 7 years ago
• dataworkshop / xgboost
  Tutorial how to use xgboost
  ☆192Updated 9 years ago
• Serena-TT / 36-methods-for-data-analysis
  ☆42Updated 4 years ago
• jstephenj14 / Monotonic-WOE-Binning-Algorithm
  Python package that optimizes information value, weight-of-evidence monotonicity and representativeness of features for credit scorecard …
  ☆116Updated 3 years ago
• weijinqian0 / feature_eda
  整理所有特征工程用到的方法，为了复用
  ☆11Updated 5 years ago
• ScienceKot / kydavra
  A python package for feature selection in python
  ☆51Updated 4 years ago
• xuman-Amy / preprocessing
  数据预处理之缺失值处理，特征选择
  ☆23Updated 6 years ago
• ikki407 / stacking
  Stacked Generalization (Ensemble Learning)
  ☆229Updated 8 years ago
• ThomasCai / tsfresh-feature-translation
  [译]tsfresh特征提取工具可提取的特征
  ☆53Updated 6 years ago
• maidoudoujiushiwo / risk_model
  risk_model
  ☆27Updated 3 years ago
• radicali94 / TimeSeries-Regression
  Comparing XGBoost, CatBoost and LightGBM on TimeSeries Regression (RMSE, R2, AIC) on two different TimeSeries datasets.
  ☆22Updated 6 years ago
• ChuanyuXue / The-Purchase-and-Redemption-Forecast-Challenge-baseline
  天池“资金流入流出预测——挑战baseline”的解决方案，线上效果143.5
  ☆100Updated 5 years ago