What is Overfitting in machine learning and why is it bad?
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Overfitting means the system learned not the pattern you want it to learn, but rather just knows it’s training data completely.
If you give it 100 pics with 50 cats and let it learn wich ones are cats without any stop criteria it will overlearn that exactly those 50 pictures are cats, but not by what the pictures have in common. It will learn stuff like “oh yeah the one with the dark blue background is a cat pic”
To prevent that you use some part of your data not for training but for quality control. You feed it only 80 pics to learn from, and use 20 only to check if they are also recognized without ever being shown to it during training.
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