Alternatives and similar repositories for k9:

Users that are interested in k9 are comparing it to the libraries listed below

• georgian-io-archive / automl_benchmark
  Distributed, large-scale, benchmarking framework for rigorous assessment of automatic machine learning repositories, projects, and librar…
  ☆30Updated 2 years ago
• MI2DataLab / pyBreakDown
  Python implementation of R package breakDown
  ☆42Updated last year
• szilard / GBM-multicore
  GBM multicore scaling: h2o, xgboost and lightgbm on multicore and multi-socket systems
  ☆20Updated 6 years ago
• szilard / awesome-GBMs
  A curated list of gradient boosting machines (GBM) resources
  ☆10Updated 5 years ago
• cemoody / flexi_hash_embedding
  PyTorch Flexible Hash Embeddings
  ☆28Updated 5 years ago
• FlorianWilhelm / lda4rec
  🧮 Extended Latent Dirichlet Allocation for Collaborative Filtering in Recommender Systems.
  ☆41Updated 2 years ago
• fastai / randomized-SVD
  demos for PyBay talk: Using Randomness to make code faster
  ☆51Updated 7 years ago
• Laurae2 / CategoricalAnalysis
  Analysis of Categorical Encodings for dense Decision Trees
  ☆41Updated 7 years ago
• navdeep-G / interpretable-ml
  Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
  ☆21Updated 2 years ago
• CamDavidsonPilon / PyDataNY_2019_tutorial
  Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference
  ☆25Updated 5 years ago
• szilard / GBM-adv-workshop-Bp19
  Advanced GBM Workshop - Budapest, Nov 2019
  ☆12Updated 5 years ago
• RAMitchell / xgb-bias-variance
  ☆14Updated 5 years ago
• luke14free / bulldog
  State management framework for Data Science & Analytics
  ☆19Updated 5 years ago
• jfpuget / metrics
  Python implementation of machine learning metrics
  ☆14Updated 7 years ago
• aslakey / CBM_Encoding
  CBM Encoding
  ☆19Updated 4 years ago
• goldentom42 / py_ml_utils
  Python utilities for Machine Learning competitions
  ☆32Updated 7 years ago
• jphall663 / secure_ML_ideas
  Practical ideas on securing machine learning models
  ☆36Updated 3 years ago
• peterhurford / vowpal_platypus
  Fast, accurate, lightweight, multi-core ML in Python, leveraging Vowpal Wabbit
  ☆21Updated 6 years ago
• maciejkula / explicit-vs-implicit
  An experiment on explicit vs implicit feedback recommenders
  ☆25Updated 7 years ago
• mllite / sklearn_explain
  Model explanation provides the ability to interpret the effect of the predictors on the composition of an individual score.
  ☆13Updated 4 years ago
• h2oai / driverlessai-tutorials
  H2OAI Driverless AI Code Samples and Tutorials
  ☆37Updated 5 months ago
• joosthub / pytorch-nlp-tutorial-eu2018
  O'Reilly AI training - London 2018
  ☆22Updated 6 years ago
• google / empirical_calibration
  ☆44Updated 11 months ago
• szilard / benchm-dl
  Playing with various deep learning tools and network architectures
  ☆70Updated 7 years ago
• jphall663 / kdd_2019
  Paper and talk from KDD 2019 XAI Workshop
  ☆20Updated 4 years ago
• tmadl / sklearn-random-rotation-ensembles
  Scikit-learn compatible implementations of the Random Rotation Ensemble idea of (Blaser & Fryzlewicz, 2016)
  ☆43Updated 9 years ago
• fastforwardlabs / couples-lime
  Notebook demonstrating use of LIME to interpret a model of long-term relationship success
  ☆24Updated 7 years ago
• arapat / sparrow
  Sparrow is a boosting algorithm implementation that is optimized for training on very large datasets and/or in the limited memory setting…
  ☆21Updated 4 years ago
• ralphhaygood / sklearn-gbmi
  scikit-learn gradient-boosting-model interactions
  ☆25Updated last year
• FlorianWilhelm / bhm-at-scale
  🪜 Bayesian Hierarchical Models at Scale
  ☆52Updated 3 years ago