Supervised Learning
Unlike unsupervised learning, supervised learning algorithms need labeled data. They learn how to automatically generate labels or predict values by analyzing various features of the data provided. For example, say you have already starred important text messages on your phone, and you want to automate the task of going through all your messages daily (considering they are important and marked already). This is a use case for supervised learning. Here, messages that have been starred previously can be used as labeled data. Using this data, you can create two types of models that are capable of the following:
- Classifying whether new messages are important
- Predicting the probability of new messages being important
The first type is called classification, while the second type is called regression. Let's learn about classification first.
Classification
Say you have two types of food, of which type 1 tastes sweet and type 2 tastes salty...
