interpretml / interpretLinks
Fit interpretable models. Explain blackbox machine learning.
☆6,535Updated last week
Alternatives and similar repositories for interpret
Users that are interested in interpret are comparing it to the libraries listed below
Sorting:
- Algorithms for explaining machine learning models☆2,527Updated 2 weeks ago
- A game theoretic approach to explain the output of any machine learning model.☆24,040Updated this week
- Visual analysis and diagnostic tools to facilitate machine learning model selection.☆4,353Updated 4 months ago
- A scikit-learn compatible neural network library that wraps PyTorch☆6,059Updated 2 weeks ago
- Distributed Asynchronous Hyperparameter Optimization in Python☆7,436Updated last month
- A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning☆7,006Updated last week
- A Python package to assess and improve fairness of machine learning models.☆2,087Updated last month
- A curated list of awesome responsible machine learning resources.☆3,805Updated last week
- Lime: Explaining the predictions of any machine learning classifier☆11,914Updated 11 months ago
- An open source python library for automated feature engineering☆7,466Updated last week
- A library of extension and helper modules for Python's data analysis and machine learning libraries.☆5,031Updated last week
- Automated Machine Learning with scikit-learn☆7,866Updated last week
- 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models☆2,903Updated 2 weeks ago
- A library for debugging/inspecting machine learning classifiers and explaining their predictions☆2,772Updated 2 months ago
- Uplift modeling and causal inference with machine learning algorithms☆5,446Updated 3 weeks ago
- A system for quickly generating training data with weak supervision☆5,873Updated last year
- A library of sklearn compatible categorical variable encoders☆2,449Updated last week
- Sequential model-based optimization with a `scipy.optimize` interface☆2,774Updated last year
- DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a uni…☆7,555Updated this week
- Book about interpretable machine learning☆4,999Updated 2 months ago
- Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.☆2,406Updated 3 weeks ago
- A python library for decision tree visualization and model interpretation.☆3,080Updated 3 months ago
- Modin: Scale your Pandas workflows by changing a single line of code☆10,197Updated this week
- Interpretability and explainability of data and machine learning models☆1,703Updated 4 months ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,413Updated 3 weeks ago
- A Python library that helps data scientists to infer causation rather than observing correlation.☆2,332Updated last year
- Hummingbird compiles trained ML models into tensor computation for faster inference.☆3,447Updated 2 months ago
- Hyper-parameter optimization for sklearn☆1,631Updated 2 months ago
- Feature engineering package with sklearn like functionality☆2,072Updated last month
- A Hyperparameter Tuning Library for Keras☆2,890Updated 6 months ago