Bias-variance decomposition and bias-variance tradeoff are closely related concepts.Bias-variance decomposition is a mathematical technique that divides the generalization error in a predictive model into two components: bias and variance.In machine learning, as you try to minimize one component of the error (e.g., bias), the other component (e.g., variance) tends to increase, and vice versa. Finding the right balance of bias and variance is key to creating an effective and accurate model. This is called the bias-variance tradeoff.Watch this video for a comprehensive explanation of the mathematical foundations behind bias- variance decomposition and the related underfitting or overfitting scenarios.