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Variance error refers to how much a result will change based on changes to the training set.a linear equation) or by a simple binary algorithm (like the true/false choices in the above tree) will often result in bias. For example, restricting your result with a restricting function (e.g. Bias error happens when you place too many restrictions on target functions.It’s possible for overfitting with one large (deep) tree. Overfitting happens for many reasons, including presence of noise and lack of representative instances.However, this simplicity comes with a few serious disadvantages, including overfitting, error due to bias and error due to variance.
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They are simple to understand, providing a clear visual to guide the decision making progress.
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