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Can anyone explain Bias in Machine Learning?

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Bias is the simplifying the assumptions made by the model to make the target function easier to approximate. Having higher bias will over-simplify the model and makes under fitting

Variance describes how scattered are the predicted value from the actual value. Higher variance will make the model overfitting. So, we need to find the trade-off between the bias and the variance for better model.

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