What is bias-variance tradeoff?
The bias-variance tradeoff is a fundamental concept in machine learning that deals with the balance between two types of errors that a model can make: bias and variance. It refers to the tradeoff between a model’s ability to accurately capture the underlying patterns in the data (low bias) and its tendency to be sensitive to variations in the training data (high variance).
In simpler terms, reducing bias typically increases variance, and vice versa. The goal is to find the right balance that minimizes the total error on unseen data. This tradeoff is crucial in model selection and training to ensure that the model generalizes well to new, unseen data.
How to Balance bias variance tradeoff
A fundamental concept in machine learning is the bias-variance tradeoff, which entails striking the ideal balance between model complexity and generalization performance. It is essential for figuring out which model works best for a certain situation and for comprehending how several models function.
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