![]() ![]() ![]() A solution to avoid overfitting is using a linear algorithm if we have linear data or using the parameters like the maximal depth if we are using decision trees. So, when building a machine learning model it is important to make sure you do not overfit your model to your training data. One could say there is no intelligence part involved in an overfit model. It will forever only be able to detect red apples that are exactly similar to the training dataset you provided any slight difference would throw it off. For example, if your model is trained to detect an apple but it is overfitting to the training data of red apples then it will not be able to detect a green apple or a black and white image of a red apple or a slightly out of shape apple. ![]()
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