Explainable Artificial Intelligence (XAI) in Python: 3 Powerful Projects You Need to Know
Source: tai_blog

Have You Ever Heard of XAI?   XAI stands for Explainable Artificial Intelligence  a research field aimed at making Machine Learning and Deep Learning models more interpretable.   One of the main criticisms of these models is that they often function as black boxes  powerful tools  indeed  but not very transparent or understandable.   And in many cases  this is true: the more complex a model is  the harder it is to interpret. However  difficult to interpret doesnt mean impossible!   Those who work in this field and understand its workings know very well that  despite their complexity  these algorithms are not inscrutable.   They are the result of mathematical calculations and computer algorithms and are interpretable and understandable.   In this guide  Ill introduce you to three fascinating XAI projects in Python that help turn these black boxes into white boxes  making them much easier to interpret!   Are you interested in the code?   I recently integrated this guide with FULL PYTHON CODE. You will find it in my Gumroad profile. It is the cheapest Python Full Tutorial that you can find on this topic. Take a look!   Explainable Artificial Intelligence (XAI) in Python: 3 Powerful Projects You Need to Know with CodeUnlock the power of Explainable Artificial Intelligence (XAI) in Python with our comprehensive guide  Explainablenardini2.gumroad.com   Before we start  please consider following me on Medium or LinkedIn. Join my Medium Newsletter to receive updates on my articles  it is totally FREE!   Get an email whenever Davide Nardini publishes.Get an email whenever Davide Nardini publishes. By signing up  you will create a Medium account if you don't alreadymedium.com   XAI in PythonMany XAI projects in Python have been increasingly populating GitHub repositories in recent years. In this guide  Ill focus on three projects that  for various reasons  stand out in the field of Explainable Artificial Intelligence.   While this wont be an exhaustive list  I hope it will still be of interest! Ive selected the following projects:   SHAP  22k stars on GitHubLIME  11k stars on GitHubInterpretML  6k stars on GitHubSHAPSHAP  which stands for SHapley Additive exPlanations  is the most widely used library for explaining how Machine Learning and Deep Learning models work.   Its based on the concept of assessing the contribution of each variable in the model to make specific predictions. It uses the Shapley value approach from game theory to estimate the importance of each feature through various iterations.   SHAP provides individual explanations for model predictions  helping users understand how each variable influenced the outcome. Its particularly useful for Machine Learning models based on decision trees and neural networks.   You will find the code in the SHAP_XAI_using_Python.ipynb file.   LIMEFollowing SHAP  we come to the second most famous library in the XAI domain: LIME.   This project has 11k stars on GitHub  although it seems to have been somewhat neglected by developers in recent years.   LIME  which stands for Local Interpretable Model-agnostic Explanations  focuses on the local interpretation of Machine Learning models.   Unlike SHAP  which uses a global approach to the model  LIME takes a local approach. It generates interpretable explanations for model predictions by focusing on a specific data instance rather than the entire model.   This approach involves generating neighboring data samples around the instance of interest and training an interpretable model (such as a decision tree or linear regression) on these samples.   The predictions of this interpretable model are then used as explanations for the original models prediction.   You will find the code in the LIME_XAI_using_Python.ipynb file.   InterpretMLThe last XAI package in Python that Ill introduce today is InterpretML  an open-source project that incorporates state-of-the-art XAI techniques.   One of the most interesting features of this library is its EBM  or Explainable Boosting Machine.   The Explainable Boosting Machine (EBM) is an interpretable algorithm that offers clear and intuitive explanations of its predictions. EBMs are regression tree-based models that approximate complex response functions while still maintaining easy interpretation.   This model allows for both local and global explanations  effectively synthesizing the two approaches previously discussed  local (LIME) and global (SHAP).   You will find the code in the InterpretML_XAI_using_Python.ipynb file.   Conclusions on XAI in PythonIn this guide  Ive discussed three XAI projects in Python that I find particularly interesting. This research field is gaining increasing importance  and its crucial to be familiar with and use it.   Among the packages Ive mentioned  the most important and comprehensive is SHAP  which continues to evolve its analytical and graphical capabilities.   The others are still significant: LIME is a historic tool  though perhaps outdated  while InterpretML is rapidly growing and currently well-supported.   Thanks for reading and see you soon :)