Unlocking the Power of Explainable AI With 5 Popular Python Frameworks
Discover how to get the most out of Explainable AI with the help of five popular Python frameworks. Learn how to uncover hidden patterns in data.
Join the DZone community and get the full member experience.Join For Free
Explainable AI, also known as XAI, is a cutting-edge branch of AI that is revolutionizing the world. It offers greater transparency and visibility into the algorithms driving machine learning, allowing us to leverage the full potential of AI technology. By harnessing state-of-the-art algorithms, we can develop tools and models that help businesses make better decisions.
Recent research by IBM has shown a significant shift in attitudes toward the ethical use of AI. In 2018, only 15% of respondents believed that a non-technical executive was the primary advocate for AI ethics. However, this number increased to 80% in a more recent study. This highlights the need for individuals and businesses to consider ethical implementation when utilizing explainable AI. Unfortunately, while 79% of CEOs are preparing to put AI ethics into practice, less than a quarter have actually implemented them.
This blog will explore popular Python frameworks, including LIME, SHAP, ELI5, Shapash, and DALEX, for AI professionals and business owners who wish to build models and tools using state-of-the-art algorithms.
By understanding how to approach projects ethically when utilizing these frameworks, we can unlock the true potential of explainable AI without risking its misuse or limitations due to a lack of understanding of its power.
Exploring Popular Explainable AI Python Frameworks
Explainable AI, or XAI, is a rapidly growing field of machine learning that focuses on providing explanations for the decisions and predictions made by artificial intelligence models. Understanding why an AI model behaves in a certain way ensures it makes sound decisions based on the data provided. If you're looking to get started with Explainable AI using Python frameworks, here are five that are worth exploring:
As the demand for explainable AI (XAI) grows, one of the most popular techniques used to understand the inner workings of predictive models is Local Interpretable Model Agnostic Explanations (LIME).
LIME is a model-agnostic approach to local explanations that can be used with both machine learning and deep learning models. It provides faithful explanations locally, explaining why a model made its prediction based on inputs from nearby points in the data set.
The process starts by taking a dataset and a prediction model, generating 5,000 samples from the dataset, and getting their target variables using the given prediction model.
After retrieving this surrogate dataset of 5,000 samples, LIME examines how close these samples are to each other to find which features contribute most heavily to each row's target variable using methods like Lasso Regression.
This unique combination of model agnosticism and local explanations makes LIME well-suited for supervised learning modeling processes as an XAI tool.
Created by Scott M. Lundberg and Su In Lee in 2017, SHAP was developed to not only provide optimal accuracy but also explain why a particular model made a prediction. This unified framework resolves the tension between the interpretability and accuracy of deep learning or ensemble models.
Derived from game theory, SHAP stands for Shapley Additive exPlanations. It uses shapely values to predict the outcomes of machine learning models and determine feature importance.
Often discussed in almost every artificial intelligence certification program, SHAP can be used in any data science application, from predictive analytics to explainable AI (XAI). Moreover, we don't have to retrain existing models, as they can be used with any machine learning model, regardless if it's an ensemble or deep learning algorithm.
Data scientists constantly strive to develop more accurate machine-learning models. However, to ensure that these models are working correctly, they must be debugged and tested in various ways. ELI5, a Python toolkit, comes in handy as it provides a uniform API for debugging and explaining black-box models.
ELI5 can be utilized for various purposes within data science and machine learning, including explaining weights and predictions of linear classifiers and regressors, depicting feature importance, highlighting text data, visualizing Grad CAM images, showing feature importance in XGBoost & LightGBM, checking weights of sklearn_crfsuite CRF models, and more.
ELI5 has built-in support for several ML frameworks, including Scikit Learn, Keras, XGBoost, LightGBM, Catboost, and Lightning.
ELI5's TextExplainer exemplifies its functionality: using the LIME algorithm (Ribeiro et al., 201) to explain text classifier predictions. At the same time, the Permutation Importance method is applied to compute feature importances for black box estimators.
Explainability Visualizations with Shapash are an interactive data science tool that helps users see and understand the results of their machine learning models engagingly and interactively.
The package was created by data scientists from French insurer MAIF and consists of explainability visualizations based on SHAP/LIME, along with a dashboard web app to access those visualizations.
Using SHAP or LIME to assess contributions in ML models, Shapash works with various models, including multiclass problems, regression, and binary classifications for Sklearn Ensemble, LightGBM, SVM, Catboost, Xgboost, and Linear models.
By exploring the model's predictions interactively, users can gain valuable insights into their data stories without writing code or creating complex plots.
Shapash allows users to inspect feature contributions for individual instances and aggregate feature importance on tabular datasets or image datasets for classification tasks with just a few clicks.
Unlock the power of machine learning with the DALEX Library. This open-source ML library is a powerful tool for data scientists, allowing them to explore various ML frameworks and compare results from local and global explainers' collections.
The minds behind this technology aimed for users to understand feature attributions or variables that confirm predictions and gauge the sensitivity of particular features.
The DALEX framework allows users to scan through any model, enabling them to explain its behavior and comprehend how complex models work.
It's part of DrWhy AI, an ebook that dives deep into Dalex's philosophical and methodological details. So you can have an even greater understanding of its capabilities.
Dalex offers various advantages, such as allowing insight into decision-making without needing access to codes used by developers. Its use makes it easier for data professionals and business folk, including those with little understanding of ML.
DALEX also helps in selecting between different predictive models by explaining why one is better than another, giving users the ability to make better decisions when it comes to their data analysis needs.
Explainable AI is quickly becoming an essential part of machine learning. With the right frameworks, you can easily understand why your AI models are making certain decisions. For example, LIME, SHAP, ELI5, Shapash, and the DALEX Framework are Python-based frameworks that provide convenient ways to explain individual predictions from any given data set.
The five mentioned above are worth exploring if you want to boost your AI skills and start with Explainable AI using Python frameworks.
Opinions expressed by DZone contributors are their own.