Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (features), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.

How can one decide on using a linear or nonlinear classifier for the dataset?

You can try with with both linear and not-linear classifiers then decides which one yields better reutls. For example you can use SVM. ... But SVM it can be used for classifying a non-linear dataset. This can be done by projecting the dataset into a higher dimension in which it is linearly separable.

Why do we use classifiers?

A classifier utilizes some training data to understand how given input variables relate to the class. ... When the classifier is trained accurately, it can be used to detect an unknown email. Classification belongs to the category of supervised learning where the targets also provided with the input data.