How to detect and prevent overfitting in a model?

Overfitting is a concept when the model fits against the training dataset perfectly. While this may sound like a good fit, it is the opposite. In overfitting, the model performs far worse with unseen data. A model can be considered an 'overfit' when it fits the training dataset perfectly but does poorly with new test datasets. On the other hand, underfitting takes place when a model has been trained for an insufficient period of time to determine meaningful patterns in the training data. Both overfitting and underfitting cannot apply themselves to largely fresh and unseen datasets.  Source: IBM Cloud Now, an optimum model may sound close to impossible to achieve; there is a formula to it. While underfitted models show less variance and more bias, overfitted models display a high