Explains model biases and reliability to build trust in AI predictions
Demonstrates techniques for interpreting linear, non-linear, and time series models
Introduces advanced XAI frameworks for deep learning, ensemble models, and computer vision
Provides practical code examples for unboxing black box models and counterfactual explanations
Focuses on ethical AI practices and fairness assessment
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Report copyright infringement by Pradeepta Mishra (Author) Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using fr