lit
ML Model Analyzer
An interactive tool for analyzing and understanding machine learning models
The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
4k stars
68 watching
357 forks
Language: TypeScript
last commit: 5 months ago machine-learningnatural-language-processingvisualization
Related projects:
| Repository | Description | Stars |
|---|---|---|
 interpretml/interpret | An open-source package for explaining machine learning models and promoting transparency in AI decision-making | 6,324 |
 h2oai/mli-resources | Provides tools and techniques for interpreting machine learning models | 483 |
 christophm/interpretable-ml-book | A comprehensive resource for explaining the decisions and behavior of machine learning models. | 4,811 |
 jphall663/interpretable_machine_learning_with_python | Teaching software developers how to build transparent and explainable machine learning models using Python | 673 |
 mayer79/flashlight | A toolset for understanding and interpreting complex machine learning models | 22 |
 csinva/imodels | An open-source package that provides interpretable machine learning models compatible with scikit-learn. | 1,406 |
| interpretml/dice | Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,373 |
| pair-code/what-if-tool | An interactive tool for exploring and understanding the behavior of machine learning models | 928 |
 cemoody/lda2vec | A framework for creating interpretable natural language models by combining word embeddings and topic modeling. | 3,152 |
 selfexplainml/piml-toolbox | A Python toolbox for developing and diagnosing interpretable machine learning models with low-code and high-code APIs. | 1,221 |
 marcotcr/lime | A tool for explaining the decisions of machine learning models | 11,663 |
 brightmart/text_classification | An NLP project offering various text classification models and techniques for deep learning exploration | 7,881 |
 ianarawjo/chainforge | An environment for battle-testing prompts to Large Language Models (LLMs) to evaluate response quality and performance. | 2,413 |
 explainx/explainx | A framework to explain and debug blackbox machine learning models with a single line of code. | 419 |
| h2oai/article-information-2019 | A framework for building and evaluating machine learning systems with high accuracy and interpretability, particularly in human-centered applications. | 13 |