Making Prediction about the future with supervised learning. The main goal in supervised learning is to learn a model from labeled training data that allows us to make prediction about unseen or future data. Here, the term supervised refers to make prediction where the desire output signals(labels) are already known.
Considering the example of email spam filterig, we can train a model using a supervised machine learning algorithm on a corpus of labeled email, e-mail that are correctly marked as spam or not-spam, to predict whether a new e-mail belons to either of the two categories. A supervised learning task with discrete class label such as in the previous e-mail spam-filtering example, is also called a classification task. Another subcategory of supervised learning is regression, where the outcomes signal is a continuous value:
There are some examples of supervised learning:-
This is nice text. , Positive
Above example show example sentences of sentiment analysis with labels.
Classification for predicting class labels
Classification is a subcategory of supervised learning where the goal is to predict the categorical class of new instance based on past observations. Those class labels are discrete, unordered values that can be understood as the group membership of the instances. The previously mentioned example of email detection represent a typical example of binary classification task where the machine learning algorithm learned a set of rules in order to distinguish between two possible classes spam and non-spam email.