Supervised and Unsupervised learning comes under machine learning they are known as the two main techniques of machine learning. However, both of them are quite different and are applicable in different scenarios and with different datasets. To get a better understanding let’s discuss it in brief.

Supervised Machine Learning

The process of supervised learning involves training models using labeled data. Models in supervised learning should find the mapping function that maps the input (X) to the output (Y).

For example- Imagine that we have an image of different types of vegetables. The goal of our supervised learning model is to identify the vegetables and classify them according to their characteristics. Therefore, for supervised learning to identify the image, we must provide input data as well as output, which means that we must train the model by identifying the vegetable’s shape, size, color, and taste. As soon as the training is complete, we will test the model by giving the new set of vegetables. Models will identify the vegetable and predict the output using appropriate algorithms.

Unsupervised Machine Learning

This technique of machine learning uses unlabelled input data to infer patterns. The purpose of unsupervised learning is to identify patterns from the input data. It does not require supervision. Instead, it identifies patterns in the data on its own.

Using the example above, we can explain unsupervised learning. Therefore, unlike supervised learning, here we will not supervise the model. We will simply provide the model with the input dataset; we will allow it to learn from the data. Using an appropriate algorithm, the model will train itself and classify the vegetables based on their similarity.

We have already understood it’s now let’s proceed further with discussing its main differences –

Supervised Learning

Unsupervised Learning

Labeled data is used to train supervised learning algorithms. Algorithms for unsupervised learning are trained by using unlabelled data.
Supervised learning uses direct feedback to determine whether it is predicting the right outcome. There is no feedback in an unsupervised learning model.
A supervised learning model predicts outcomes. Learning from unsupervised data uncovers hidden patterns
The model is provided with input and output data in supervised learning. Unsupervised learning relies solely on input data.
Supervised learning aims to train the model to be able to predict what the output will be given new data. Learning unsupervised is aimed at discovering hidden patterns and providing useful insights from a dataset that is unknown.

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