What is supervised machine learning

Supervised machine learning, as the name suggests, involves the use of a supervisor or a tutor who guides the computer in its learning process. The supervisor provides the algorithm with labeled training data to help it understand the relationships between the input data and the output data.

In supervised machine learning, the algorithm is provided with a set of input data, along with the correct output for each input. The algorithm then learns to associate the input data with the correct output data. This association is learned by the algorithm through the use of a mathematical function or model that maps the input data to the output data.

The supervised learning algorithm uses the labeled data to build a model that can predict the output for new, unseen input data. The goal of the algorithm is to minimize the difference between the predicted output and the actual output.

As an example, let’s say we are trying to build a spam classifier for email messages. The algorithm would be trained using a set of labeled data consisting of email messages, where each message is labeled as spam or not spam. The algorithm would learn to identify patterns in the data that distinguish between spam and non-spam messages. Once the algorithm is trained, it can then be used to classify new, unseen email messages as spam or not spam.

The performance of the algorithm depends on the quality of the training data. The more diverse and representative the training data is, the better the performance of the algorithm will be.

Some common algorithms used in supervised machine learning include linear regression, logistic regression, decision trees, random forests, and neural networks.

Supervised machine learning is widely used in applications such as image and speech recognition, recommendation systems, fraud detection, and natural language processing.

In conclusion, supervised machine learning is a powerful approach to machine learning that relies on a supervisor to guide the algorithm in its learning process. It is widely used in various applications and requires high-quality training data to achieve optimal performance.