model interpretability libraries Flashcards
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Model interpretability
Model interpretability is crucial for understanding how machine learning models make predictions and gaining insights into their decision-making process. Model interpretability is a critical aspect of machine learning, particularly in domains where transparency, accountability, and fairness are paramount. It empowers users to gain insights into AI systems, make informed decisions, and use AI responsibly in real-world applications.
- SHAP (SHapley Additive exPlanations)
- SHAP is a powerful library that provides unified explanations for a wide range of machine learning models. - It’s based on cooperative game theory and calculates Shapley values to measure the impact of each feature on a model’s output. - SHAP supports various model types, including tree-based models, ensemble models, and deep learning models.
- Lime (Local Interpretable Model-agnostic Explanations)
- Lime is a model-agnostic interpretability library that provides local explanations for individual predictions. - It approximates the behavior of complex models using locally interpretable surrogate models. - Lime is particularly useful for explaining black-box models and offers support for tabular data, text data, and images.
- ELI5 (Explain Like I’m 5)
- ELI5 is a simple and easy-to-use library that offers model explanations and feature importances for various models. - It provides a unified API to explain scikit-learn models, XGBoost, LightGBM, and more. - ELI5 supports different interpretability techniques like permutation importance, feature weights, and LASSO-based explanations.
- Tree Interpreter
- Tree Interpreter is a specialized library for interpreting tree-based models like decision trees and random forests. - It decomposes model predictions into contributions from individual decision paths, showing how each feature influences the output.
- Yellowbrick
- Yellowbrick is a visualization library that complements other interpretability libraries by providing visual diagnostics and explanations. - It offers features like visualizing feature importances, residuals, and prediction errors. - Yellowbrick integrates well with scikit-learn and can be used alongside other interpretability libraries.
- Skater
- Skater is a Python library for model interpretation and visualization, with a focus on supporting complex, high-dimensional data. - It offers multiple techniques, including partial dependence plots, sensitivity analysis, and feature importance plots. - Skater can handle tabular data, text data, and image data for model interpretability.
- AIX360 (AI Explainability 360)
- AIX360 is an IBM open-source toolkit that provides various explainability algorithms for machine learning models. - It includes interpretable models, rule-based explainers, and other explainability techniques. - AIX360 is a comprehensive library that supports model interpretability for multiple domains.
- SHAP for Deep Learning (TF SHAP)
- If you work extensively with deep learning models on macOS, you can use the SHAP implementation specifically designed for TensorFlow models. - TF SHAP allows you to apply SHAP values to understand the impact of each feature on deep learning model predictions.
- Understanding Model Decisions
Model interpretability refers to the process of comprehending the reasons behind a machine learning model’s predictions or decisions.
- Explaining Feature Importance
It involves identifying and quantifying the importance of individual input features in influencing the model’s output.
- Human-Readable Explanations
Model interpretability aims to provide human-readable explanations that non-experts can understand and trust.
- Identifying Key Patterns
Interpretability techniques help in identifying key patterns and relationships between input features and the model’s predictions.
- Insight into Decision Boundaries
It allows understanding how a model separates different classes or categories in the input space.