In this Bias and Variance In Statistics tutorial, we will understand what Bias and Variance stand for. Bias and Variance is the foundation concept of Machine Learning and Data Science and Analysis. The tutorial will explain Bias and Variance by taking a simple day-to-day example.
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Bias is the amount that a model’s prediction differs from the target value, compared to the training data. Bias error results from simplifying the assumptions used in a model so the target functions are easier to approximate. Variance indicates how much the estimate of the target function will alter if different training data were used.
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