What is the difference between supervised and unsupervised machine learning?

What is the difference between supervised and unsupervised machine learning ?

SUPERVISED LEARNING : What do we understand about Supervision? Supervision is learning something under the guidance of a teacher or some supervisor, who can judge us whether we are doing things right or not. Similarly, in supervised learning, we have a set of labeled data while we are training an algorithm. Now what do we understand by labeled data?

Labeled data mean that the target data is tagged with the answer, that the algorithm on which we are working, should come up with. So, for example, a labeled dataset of bikes would tell the algorithm to tell us about R15, Pulsar, Splendor. And whenever a new image is being shown, the algorithm compares it to the training data set to come up with a result and to predict the correct label.
UNSUPERVISED LEARNING: This technique is used where deep learning model is handed the data set with no explicit instructions, that what to do with it. The model then tries to automatically find the structure in data by extracting the features and analyzing the structure.

It can organize the data in various ways like Clustering, Anomaly Detection, Association, Auto-Encoders.

It’s difficult to calculate the accuracy of the algorithm under unsupervised learning.

ref: https://medium.com/datadriveninvestor/supervised-and-unsupervised-learning-7281050992a0

I’ll try to explain it in an intuitive way through two examples:


An example of supervised learning algorithm can be a credit card fraud detection, where the learning algorithm is presented with credit card transactions classified as normal or suspicious.

The classification is done by hand and the algorithm learns by it. At the end the algorithm produces a decision model that classifies future transactions as normal or suspicious.


Unsupervised learning discovers hidden patterns in the data. An example of unsupervised learning is an item-based recommendation system, where the learning algorithm discovers similar items bought together.

For example, people who bought book “Harry Potter” also bought book “Lord of Rings”.

The usage of labeled datasets is the crucial difference between the two methodologies. Simply put, supervised learning algorithms use labeled input and output data, whereas unsupervised learning algorithms do not.

The algorithm “understands” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer in supervised learning.

While supervised learning models are more precise than unsupervised learning models , they necessitate human interaction to identify the data correctly.

A supervised learning model , for example, can predict the length of your travel based on different factors like the time of day, weather conditions, and other factors. But first, you’ll have to teach it that travel time is longer in rainy conditions.

On the other hand, Unsupervised learning models function independently to uncover the structure of unlabeled data. It’s worth noting that validating output still necessitate human intervention.

An unsupervised learning model , for example, can detect that online buyers frequently purchase groups of products simultaneously. However, a data analyst would need to confirm that grouping baby garments with an order of diapers, applesauce, and sippy cups makes sense for a recommendation engine.