Alternatives and similar repositories for controlburn

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

• zzzace2000 / GAMs
  Multiple Generalized Additive Models implemented in Python (EBM, XGB, Spline, FLAM). Code for our KDD 2021 paper "How Interpretable and T…
  ☆12Updated 3 years ago
• a-lucic / focus
  ☆16Updated 3 years ago
• StatMixedML / DGBM
  Distributional Gradient Boosting Machines
  ☆27Updated 2 years ago
• Liam-E2 / RotationForest
  A python implementation of the Rotation Forest algorithm per https://arxiv.org/abs/1809.06705.
  ☆20Updated 5 years ago
• andruekonst / egbm-gam
  A straightforward implementation of EGBM-based Generalized Additive Model
  ☆13Updated 4 years ago
• nickfrosst / neural_additive_models
  stand alone Neural Additive Models, forked from google-reasearch for easy import to colab
  ☆28Updated 4 years ago
• syanga / pycit
  (Conditional) Independence testing & Markov blanket feature selection using k-NN mutual information and conditional mutual information es…
  ☆30Updated 5 years ago
• realrate / Causing
  Causing: CAUsal INterpretation using Graphs
  ☆58Updated 3 weeks ago
• big-data-lab-umbc / ensemble_causality_learning
  ☆10Updated 4 years ago
• yaronshap / GloballyConsistentRules
  Implementation of algorithms from the paper "Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application…
  ☆25Updated 3 years ago
• StatMixedML / Py-BoostLSS
  An extension of Py-Boost to probabilistic modelling
  ☆23Updated 2 years ago
• tomlinsonk / choice-set-opt
  Code accompanying our ICML 2020 paper on choice set optimization in group decision-making.
  ☆11Updated 4 years ago
• dlegor / rad
  Implementation of Robust PCA and Robust Deep Autoencoder over Time Series
  ☆14Updated 5 years ago
• mmschlk / iXAI
  Fast and incremental explanations for online machine learning models. Works best with the river framework.
  ☆55Updated 6 months ago
• moorecsys / moorecsys.github.io
  Tutorial on Multi-Objective Recommender Systems @ KDD 2021
  ☆19Updated 2 years ago
• autonlab / esrnn
  Hybrid ES-RNN models for time series forecasting
  ☆19Updated 4 years ago
• StatMixedML / Hyper-Trees
  Forecasting with Hyper-Trees
  ☆18Updated 8 months ago
• Neal-Liu-Ziyi / Concept_Drift_Handling
  ☆12Updated 2 years ago
• adaa-polsl / RuleXAI
  A rule-based aproach to explain the output of any machine learning model
  ☆15Updated last year
• SantiagoEG / FCBF_module
  Fast Correlation-Based Feature Selection
  ☆31Updated 8 years ago
• david26694 / sktools
  Helpers for scikit learn
  ☆16Updated 2 years ago
• RifleZhang / CORD_CPD
  Correlation-aware Change-point Detection via Graph Neural Networks
  ☆16Updated 4 years ago
• Thartvigsen / StopAndHop
  Code for Stop&Hop, a method for learning to classify irregularly-sampled time series early
  ☆18Updated 8 months ago
• yuachen / CausalDA
  Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…
  ☆18Updated 4 years ago
• alan-turing-institute / rPSMF
  Code for Probabilistic Sequential Matrix Factorization
  ☆15Updated 4 years ago
• SelfExplainML / GamiNet
  GAMI-Net: Generalized Additive Models with Structured Interactions
  ☆31Updated 3 years ago
• soroushzargar / DAPS
  Implementations of methods proposed in the paper "Conformal Prediction Sets for Graph Neural Networks"
  ☆14Updated 2 years ago
• taposh / forecast
  Forecasting library in python
  ☆13Updated 5 years ago
• kamilest / conformal-rnn
  Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.
  ☆74Updated 7 months ago
• GuillaumeStaermanML / FIF
  Source code for the ACML 2019 paper "Functional Isolation Forest".
  ☆21Updated 2 years ago