An overfitting scenario is when a model performs very well on training data but poorly on test data. The noise that the machine learning model learns along with the patterns will have a detrimental impact on the model
An overfitting scenario is when a model performs very well on training data but poorly on test data. The noise that the machine learning model learns along with the patterns will have a detrimental impact on the model's performance on test data. When using nonlinear models with a nonlinear decision boundary, the overfitting issue typically arises. In SVM, a decision boundary could be a hyperplane or a linearly separable line.
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