What is Supervised Machine Learning?

Supervised learning is a machine learning approach where models train on labeled data to make predictions. It is used for classification (e.g., spam detection) and regression (e.g., stock price prediction). Key algorithms include decision trees, SVM, neural networks, and Naïve Bayes.

Machine learning has transformed various industries, from healthcare to finance, enabling systems to learn from data and make intelligent decisions. One of the fundamental types of machine learning is supervised learning, which involves training a model using labeled data.

This article will explore supervised learning, its types, key algorithms, advantages, challenges, real-world applications, and future trends.

What is Supervised Learning?

Supervised learning functions as a machine learning technique allowing algorithms to learn from training data sets with labels to transform inputs into desired outputs. The main goal seeks to reduce mistakes while ensuring effective performance on unknown data.

The learning process occurs through input-output pair examination followed ...