MACHINE LEARNING
INTRODUCTION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Variance
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Overfitting
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Underfitting
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Bias
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Detailed explanation-1: -Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.
Detailed explanation-2: -There are two main types of errors present in any machine learning model. They are Reducible Errors and Irreducible Errors. Irreducible errors are errors which will always be present in a machine learning model, because of unknown variables, and whose values cannot be reduced.
Detailed explanation-3: -Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.