Overfitting generally occurs when a model begins to memorize training data rather than learning to generalize from the observed trend in the training data. Overfitting is the model, which generally takes the form of making an overly complex model to explain idiosyncrasies in the data under study. It is especially likely in cases where learning was performed too long or where training examples are rare, causing the learner to adjust to very specific random features of the training data, that have no causal relation to the target function. In this process of overfitting, the performance on the training examples still increases while the performance on unseen data becomes worse.
About Overfitting
