The Bias-Variance Tradeoff is a fundamental concept in machine learning that describes the tradeoff between the prediction error due to bias and variance. In simple terms, bias refers to the underfitting of a model, whereas variance refers to the overfitting of a model.

To better understand this tradeoff, consider the task of predicting a continuous variable, such as the price of a house, based on a set of input features. A model with high bias will make oversimplified assumptions about the relationship between the input features and the target variable, resulting in a large prediction error. On the other hand, a model with high variance will fit the training data too closely, resulting in a low training error but a high prediction error on new data.

The goal of machine learning is to find the right balance between bias and variance, where the model is complex enough to capture the underlying relationships in the data, but not so complex that it overfits the training data. This balance can be achieved through techniques such as cross-validation, regularization, and ensemble methods.

In summary, the Bias-Variance Tradeoff is a critical concept in machine learning that helps us understand the tradeoff between underfitting and overfitting. By finding the right balance between bias and variance, we can build models that generalize well to new data and make accurate predictions.