bias-variance tradeoff
The bias-variance tradeoff is a concept in machine learning that describes the balance between two types of errors when creating models. Bias refers to the error caused by oversimplifying a model, which can lead to missing important patterns (underfitting). Variance, on the other hand, is the error from capturing too much noise in the data, making the model overly complex (overfitting). A good model finds a middle ground, minimizing both types of errors to generalize well to new data, ensuring accurate predictions without being too rigid or too flexible.
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The bias-variance tradeoff is a concept in machine learning and statistics that describes the balance between two types of errors in a model. Bias refers to errors due to oversimplifying the model, often leading to systematic mistakes. Variance, on the other hand, refers to errors resulting from a model that is too complex, capturing noise rather than the underlying pattern. A good model tries to minimize both types of errors; too much bias makes it inaccurate, while too much variance makes it inconsistent. Finding the right balance helps create a model that generalizes well to new data.