TensorFlow Interpretability Techniques Explained

There are several model interpretability techniques in TensorFlow, which include:

1. SHAP (Shapley Additive explanations): SHAP is an interpretive technique for deep learning models that helps users understand the decision-making process of the model by explaining its outputs.
2. LIME is a local interpretability technique that can explain the decision process of a model on a specific sample and provide interpretability for model predictions.
3. Integrated Gradients is a method that explains the output of deep learning models by integrating different parts of the input. It helps users understand the key features and decision-making process of the model.
4. Shapley values: Shapley values are an explanatory technique based on game theory that helps users understand how the output of a deep learning model is determined by the input features.
5. Sensitivity Analysis is a method that evaluates the stability and sensitivity of model outputs by making small changes to input features. It helps users understand how changes in different features affect the output of the model.