In other words, a model that overfit fails to capture the relationship between the data: it knows how to effectively predict the data that was used for training, however it does not know how to predict correctly when it comes to news observations.

Overfitting is a very common problem in Machine Learning that occurs when the model performs well on training data but generalizes poorly to unseen data. The high variance of the model performance is an indicator of an overfitting problem.

Dear Muhammad Imad ,

Overfitting makes the model relevant to its data set only, and irrelevant to any other data sets. Some of the methods used to prevent overfitting include ensembling, data augmentation, data simplification, and cross-validation.

https://corporatefinanceinstitute.com/resources/data-science/overfitting/

Regards,

Shafagat

Overfitting is a concept in data science, which occurs when a statistical model fits exactly against its training data. When this happens, the algorithm, unfortunately, cannot perform accurately against unseen data, defeating its purpose.

There are some solutions such as:

GOOD LUCK

Overfitting is when a machine learning model becomes too specialized to the training data and performs poorly on new, unseen data. To avoid overfitting, one can increase the size of the training data, use regularization techniques, cross-validation, early stopping, or data augmentation techniques to improve the model's ability to generalize.