Bias and variance tradeoff is crucial in model selection from "summary" of Machine Learning by Ethem Alpaydin
When choosing a model for a machine learning task, one must consider the tradeoff between bias and variance. Bias refers to the error introduced by approximating a real-world problem, which may result in oversimplification of the model. On the other hand, variance refers to the model's sensitivity to fluctuations in the training data, which can lead to overfitting. A high-bias model, also known as an underfitting model, fails to capture the underlying structure of the data. This can result in poor performance on both the training and test data. In contrast, a high-variance model, or overfitting model, performs well on the training data but poorly on the test d...
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