Linking pages
- GitHub - jphall663/awesome-machine-learning-interpretability: A curated list of awesome machine learning interpretability resources. https://github.com/jphall663/awesome-machine-learning-interpretability 0 comments
- GitHub - r0f1/datascience: Curated list of Python resources for data science. https://github.com/r0f1/datascience 0 comments
- GitHub - ml-tooling/best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. https://github.com/ml-tooling/best-of-ml-python 0 comments
- Machine Learning Toolbox https://amitness.com/toolbox/ 0 comments
- GitHub - academic/awesome-datascience: An awesome Data Science repository to learn and apply for real world problems. https://github.com/bulutyazilim/awesome-datascience 0 comments
Linked pages
- Interpretable Machine Learning https://christophm.github.io/interpretable-ml-book/ 45 comments
- [1811.10154] Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead https://arxiv.org/abs/1811.10154 14 comments
- GitHub - h2oai/h2o-3: H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. https://github.com/h2oai/h2o-3 0 comments
- GitHub - interpretml/interpret: Fit interpretable models. Explain blackbox machine learning. https://github.com/interpretml/interpret 0 comments
- GitHub - tmadl/sklearn-expertsys: Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models https://github.com/tmadl/sklearn-expertsys 0 comments
- Very Simple Classification Rules Perform Well on Most Commonly Used Datasets | SpringerLink https://link.springer.com/article/10.1023/A:1022631118932 0 comments