Business Intelligence
The bias-variance tradeoff is a fundamental concept in machine learning that refers to the balance between two types of errors that affect the performance of predictive models: bias error, which arises from overly simplistic assumptions in the learning algorithm, and variance error, which stems from excessive complexity in the model. Understanding this tradeoff is crucial for building effective supervised and unsupervised learning algorithms, as it helps determine the optimal level of model complexity to achieve the best predictive accuracy while minimizing overfitting and underfitting.
congrats on reading the definition of bias-variance tradeoff. now let's actually learn it.