What is the difference between supervised learning and unsupervised learning? [closed]

Answer 1

Supervised learning, given the data with an answer.

Given email labeled as spam/not spam, learn a spam filter.

Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.

Unsupervised learning, given the data without an answer, let the pc to group things.

Given a set of news articles found on the web, group the into set of articles about the same story.

Given a database of custom data, automatically discover market segments and group customers into different market segments.

Reference

Answer 2

I have always found the distinction between unsupervised and supervised learning to be arbitrary and a little confusing. There is no real distinction between the two cases, instead there is a range of situations in which an algorithm can have more or less 'supervision'. The existence of semi-supervised learning is an obvious examples where the line is blurred.

I tend to think of supervision as giving feedback to the algorithm about what solutions should be preferred. For a traditional supervised setting, such as spam detection, you tell the algorithm "don't make any mistakes on the training set"; for a traditional unsupervised setting, such as clustering, you tell the algorithm "points that are close to each other should be in the same cluster". It just so happens that, the first form of feedback is a lot more specific than the latter.

In short, when someone says 'supervised', think classification, when they say 'unsupervised' think clustering and try not to worry too much about it beyond that.

Answer 3

Supervised Learning is basically a technique in which the training data from which the machine learns is already labelled that is suppose a simple even odd number classifier where you have already classified the data during training . Therefore it uses "LABELLED" data.

Unsupervised learning on the contrary is a technique in which the machine by itself labels the data . Or you can say its the case when the machine learns by itself from scratch.

Answer 4

In Supervised Learning we know what the input and output should be. For example , given a set of cars. We have to find out which ones red and which ones blue.

Whereas, Unsupervised learning is where we have to find out the answer with a very little or without any idea about how the output should be. For example, a learner might be able to build a model that detects when people are smiling based on correlation of facial patterns and words such as "what are you smiling about?".

Answer 5

Supervised learning

You have input x and a target output t. So you train the algorithm to generalize to the missing parts. It is supervised because the target is given. You are the supervisor telling the algorithm: For the example x, you should output t!

Unsupervised learning

Although segmentation, clustering and compression are usually counted in this direction, I have a hard time to come up with a good definition for it.

Let's take auto-encoders for compression as an example. While you only have the input x given, it is the human engineer how tells the algorithm that the target is also x. So in some sense, this is not different from supervised learning.

And for clustering and segmentation, I'm not too sure if it really fits the definition of machine learning (see other question).

Answer 6

supervised learning

supervised learning is where we know the output of the raw input, i.e the data is labelled so that during the training of machine learning model it will understand what it need to detect in the give output, and it will guide the system during the training to detect the pre-labelled objects on that basis it will detect the similar objects which we have provided in training.

Here the algorithms will know what's the structure and pattern of data. Supervised learning is used for classification

As an example, we can have a different objects whose shapes are square, circle, trianle our task is to arrange the same types of shapes the labelled dataset have all the shapes labelled, and we will train the machine learning model on that dataset, on the based of training dateset it will start detecting the shapes.

Un-supervised learning

Unsupervised learning is a unguided learning where the end result is not known, it will cluster the dataset and based on similar properties of the object it will divide the objects on different bunches and detect the objects.

Here algorithms will search for the different pattern in the raw data, and based on that it will cluster the data. Un-supervised learning is used for clustering.

As an example, we can have different objects of multiple shapes square, circle, triangle, so it will make the bunches based on the object properties, if a object has four sides it will consider it square, and if it have three sides triangle and if no sides than circle, here the the data is not labelled, it will learn itself to detect the various shapes