Exascale Computing
The bias-variance tradeoff is a fundamental concept in statistical modeling and machine learning that describes the balance between two types of errors when building predictive models: bias and variance. Bias refers to the error introduced by approximating a real-world problem, which can cause an algorithm to miss the relevant relations between features and target outputs, while variance refers to the error introduced by too much complexity in the model, causing it to model the random noise in the training data instead of the intended outputs. Understanding this tradeoff is essential for effective dimensionality reduction and feature selection, as it helps determine how many features to include and which ones to retain to minimize prediction errors without overfitting.
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