What is the difference between lime and Shap?
SHAP and LIME are both popular Python libraries for model explainability. SHAP (SHapley Additive exPlanation) leverages the idea of Shapley values for model feature influence scoring. Simply put, LIME is fast, while Shapley values take a long time to compute.
What models does Shap support?
SHAP provides multiple explainers for different kind of models. TreeExplainer: Support XGBoost, LightGBM, CatBoost and scikit-learn models by Tree SHAP. DeepExplainer (DEEP SHAP): Support TensorFlow and Keras models by using DeepLIFT and Shapley values. GradientExplainer: Support TensorFlow and Keras models.
How does explain your model with Shap and lime?
At a high level, the way both of these work is that you give your training data and model to an “explainer”, and then you’re later able to pass any observation to the “explainer” which will tell you the feature importance of that model. That might sound like gobbledegook right now, but we’ll make this extra clear with our Titanic example!
How to use lime to understand sklearn models predictions?
explain_instance () ¶ The LimeTabularExplainer instance has a method named explain_instance () which takes as input random data sample and method which predicts output as input and generates explanation object. This Explanation object has information about feature contribution to this particular prediction.
Is the syntax for Shap and lime the same?
The syntax for using LIME is a little bit different, but conceptually speaking, both SHAP and LIME are doing a lot of similar stuff. Let’s take a look at what LIME shows us for each person.
Why is Shap used in interpretable machine learning?
SHAP connects LIME and Shapley values. This is very useful to better understand both methods. It also helps to unify the field of interpretable machine learning. SHAP has a fast implementation for tree-based models. I believe this was key to the popularity of SHAP, because the biggest barrier for adoption of Shapley values is the slow computation.