What Is Supervised Learning?

Here’s what supervised learning is all about, how it works, and its applications.

How Does Supervised Learning Work?

In supervised learning, an AIalgorithmis fed training data (inputs) with clear labels (outputs).

Based on the training set, the AI learns how to label future inputs of unlabeled data.

Ideally, the algorithm will improve its accuracy as it learns from past experiences.

Once you’ve provided the training data, you would test the algorithm by showing it shapes without labels.

The AI will then use its knowledge from the training set to assign the appropriate labels/outputs to each shape.

The training data set must also be diverse enough for the algorithm to identify slight pattern variances.

That’s because it could indicate overfitting, which is when the training and test data are too similar.

In semi-supervised learning, part of the input data is already labeled.

A supervisor must also test the algorithm for accuracy.

Unlike unsupervised learning, supervised learning algorithms can’t classify data independently.

Supervised learning algorithms can be combined withneural networksto reassess their own outputs and fine-tune themselves.

The difference is that in self-supervised learning, humans don’t provide labels.

Supervised learning is most useful when you have objects that you definitely want to train the program to identify.

For example, autonomous car programmers really want vehicles to know a stop sign when they see one.

Unsupervised learning’s system is more for building understanding of a particular field (e.g., physics).