FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Underfitting refers to a model that can neither model the training data nor generalize to new data. An underfit machine learning model is not a suitable model and will be obvious as it will have poor performance on the training data.

Detailed explanation-2: -Underfitting means that your model makes accurate, but initially incorrect predictions. In this case, train error is large and val/test error is large too. Overfitting means that your model makes not accurate predictions. In this case, train error is very small and val/test error is large.

Detailed explanation-3: -Your model is underfitting the training data when the model performs poorly on the training data. This is because the model is unable to capture the relationship between the input examples (often called X) and the target values (often called Y).

Detailed explanation-4: -It will make inaccurate predictions when given new data, making the model useless even though it is able to make accurate predictions for the training data. This is called overfitting. The inverse is also true. Underfitting happens when a model has not been trained enough on the data.

Detailed explanation-5: -What is underfitting? Underfitting is a scenario in data science where a data model is unable to capture the relationship between the input and output variables accurately, generating a high error rate on both the training set and unseen data.