eXplainable-AI-XAI-Basics

eXplainable Artificial Intelligence (XAI) Basic Algorithms on Iris Dataset Explainable Artificial Intelligence (XAI) aims to make AI systems' operations and results understandable to humans. It addresses the "black box" nature of complex AI models, ensuring transparency and interpretability. XAI helps in building trust by providing clear explanations for AI decisions, often through visualizations or simplified narratives. It is crucial for ethical and fair decision-making in high-stakes areas like healthcare and finance, where understanding AI's reasoning is vital. Feature Importance: This method ranks the significance of different features in a predictive model based on their influence on the model's output. It highlights which features are most relevant in determining the predictions. SHAP (SHapley Additive exPlanations): SHAP values explain the prediction of an instance by computing the contribution of each feature to the prediction. It is based on game theory and provides a unified approach to explain the output of any machine learning model. Surrogate Models: These are interpretable models (like linear regression or decision trees) used to approximate the predictions of a complex, black-box model. They help in understanding the more complex model by providing a simpler, understandable approximation. Permutation Importance: This technique measures the increase in the prediction error of the model after we permute the feature's values, indicating how much the model depends on the feature. LIME (Local Interpretable Model-Agnostic Explanations): LIME explains individual predictions by approximating the model locally with an interpretable one, helping to understand how each feature affects a prediction. Anchor: This algorithm provides model-agnostic, high-precision explanations by identifying 'anchors' – sets of features that sufficiently anchor the prediction, ensuring it remains unchanged regardless of other features' values.