tree-based-models

There are 11 repositories under tree-based-models topic.

  • kyosek/CFXplorer

    CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.

    Language:Python5301

  • SonghuaHu-UMD/Explainable_AI_Comparison

    Language:Python5103

  • eth-sri/drs

    [NeurIPS 2022] (De-)Randomized Smoothing for Decision Stump Ensembles

    Language:Terra4501

  • skswar/Explainable_ML_for_Production_Downtime_Shap

    👨‍💻 This repository shows how machine learning and SHAP can be leveraged to understand the reasons of production downtime ⌛

    Language:Jupyter Notebook1100

  • Chinmaya-3141/Machine-Learning-with-Hyperopt-on-Emissions-Data

    Project started as submission for MSE course at Praxis Business School, then further worked upon to implement machine learning models with hyperparameter tuning.

    Language:Jupyter Notebook0100

  • point-de-vyu/data-analytics-projects

    Data Science portfolio

    Language:Jupyter Notebook0100
  • mn-mat-dis

    romrawinjp/mn-mat-dis

    Project page for "Physics-informed graph neural networks accelerating microneedle simulations towards novelty of micro-nano scale materials discovery" as a part of Romrawin Chumpu's master thesis and publication.

    Language:JavaScript0200

  • hanfei1986/Tree-based-and-neural-network-regressors

    Usually tree-based and neural network regressors work better for regression tasks than linear regression models, because they can capature complex or subtle non-linear patterns in data.

    Language:Jupyter Notebook10

  • MortenBlorstad/StableTrees

    Tree-based models are appealing for price modeling due to their high performance but they can be unstable. Due to competition between insurers, unstable models increase the risk that the overall premium is too small to cover the losses. The thesis propose various strategies for improving the stability of tree-based models.

    Language:Jupyter Notebook30

  • numanwaziri/EEG-Schizophrenia-Detection

    This project uses EEG data to detect schizophrenia, achieving a robust classifier with LGBM, boasting a ROC AUC of 95.96% and an accuracy of 90%

    Language:Jupyter Notebook10

  • RIT-MESH/Master-Data-Mining-in-Data-Science-Machine-Learning

    In this course, you will get advanced knowledge on Data Mining. This course begins by providing you the complete knowledge about the introduction of Data Mining. This course is a complete package for everyone wanting to pursue a career in data mining.

    Language:Jupyter Notebook10