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Can anyone explain bias and variance in Machine Learning?

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In simple words, Bias is the simplifying the assumptions made by the model to make the target function easier to approximate. 

Variance depends on the performance of a model on the trained dataset and the dataset that it is not trained on. Variance describes how scattered are the predicted value from the actual value.

Having a higher bias will over-simplify the model and makes underfitting. The higher variance will make the model overfitting. So, we need to find the trade-off between the bias and the variance for a better model.

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