Bias-Variance Tradeoff

The Bias-Variance Tradeoff is a fundamental concept in machine learning that describes the tradeoff between two types of errors that affect model performance: bias and variance. Bias refers to the error due to overly simplistic assumptions in the learning algorithm, which can lead to underfitting, while variance refers to the error due to excessive complexity in the model, which can result in overfitting. Striking the right balance between bias and variance is crucial for achieving good generalization on unseen data. Common use cases include model selection and tuning hyperparameters to optimize performance in predictive modeling tasks.