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Bias and variance tradeoff is important for finding the right balance in model performance from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Bias and variance are two sources of error in the prediction of machine learning models. Bias is error caused by the simplifying assumptions made by a model. A high-bias model may oversimplify the data and fail to capture important patterns, leading to underfitting. On the other hand, variance is error caused by the model's sensitivity to fluctuations in the training data. A high-variance model may capture noise in the training data and fail to generalize well to new, unseen data, leading to overfitting. The bias-variance tradeoff is the balance between these two sources of error. A model with high bias tends to have low variance, while a model with high variance tends to have low bias. Finding the right balance between bias and variance is crucial for achieving optimal model performance. If a model has high bias, it may be too simple to capture the complexity of the data, resulting in poor predictive performance. On the other hand, ...
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