What is Overfitting and when does it occur?
If you’re in a foreign country and someone steals something of yours, you would possibly say that everybody may be a thief. This is an overgeneralization, and, in machine learning, is named “overfitting”. This means that machines do an equivalent thing: they will perform well when they’re working with the training data, but they cannot generalize them properly. For example, in the following figure you’ll find a high degree of life satisfaction model that overfits the data, but it works well with the training data.
Overfitting occurs when the model is very complex for the amount of training data given.
Solution for overfitting
To solve the overfitting problem, you should do the following: –
- Gather more data for “training data”
- Reduce the noise level
- Select one with fewer parameters
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