Supervised Learning

What is supervised learning?

We humans learn from our past experiences. However, machines (or computers) don’t have experiences. They learn from data, which represents past experiences in an application domain. In supervised learning, our focus is to learn a target function that can be used to predict the values (labels) of discrete class attributes. The task is usually known as supervised learning.

At its core, supervised learning is a type of machine learning where the algorithm learns from labeled training data to make predictions or decisions. In this paradigm, the algorithm is trained on a dataset that contains input data as well as corresponding desired outputs. The goal is to learn a mapping function from the input to the output, allowing the algorithm to make predictions on unseen data. Essentially, the algorithm is supervised by the provided dataset, guiding it to learn the patterns and relationships within the data.

Supervised learning is a two-step process:

1. Learning or training: Learn a model using the training data (with labels).

2. Testing: Test the model using unseen test data (without labels) to assess the model’s accuracy.