Supervised Learning

Definition

Supervised learning is a type of machine learning where the model is trained on a labeled dataset. It means that the input data used for training also includes the correct output or result.

Explain Like I'm 5

Imagine you are learning to recognize different shapes. Your teacher shows you a circle and says "This is a circle." Then she shows you a square and says, "This is a square." This way, by telling you what each shape is, or giving you the 'label', she's teaching you in a supervised way. Supervised learning in machines works pretty much like this!

Visualization

Supervised Learning

Digging Deeper

In supervised learning, we have an algorithm that learns from input data and its corresponding output. The goal of the model is to learn a mapping function from inputs to outputs. The learning process continues until the model achieves an acceptable level of performance. There are two types of supervised learning: regression (predicting continuous values) and classification (predicting discrete values). For example, predicting house prices based on various features (like size, location) would be a regression problem, while classifying emails into spam or not spam would be a classification problem.

Applications

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