Overfitting of a model means model performs wells on a training data but fails to perform on a testing data it learns the outliers of a data while underfitting means model do not perform on both training and testing dataLinear regression model trying to fit non linear points is an example of underfitting model

I learned about Overfitting and Underfitting of a Model. 1. Underfitting: If a model is too simple that it cannot even understand the pattern of the data, it is known as Underfitting. The model doesn't perform well on the training as well as new data.2: Overfitting: If a model is too complex that it learns the noise and extra details (outliers) of the data, it is known as Overfitting. The model performs well on the training data but performs poorly on the training/ new data.

once again done overfitting and underfitting

Done

learn overfitting and underfitting

nice

This lecture covers the important topics of overfitting and underfitting which will more discussed in the coming lectures.

I learned (Overfitting and Underfitting).

I learned Overfitting and underfitting these concepts are clear.

Suppose the model learns the training dataset, like the Y student. They perform very well on the seen dataset but perform badly on unseen data or unknown instances. In such cases, the model is said to be Overfitting.