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

Answer 1

I can tell you an example.

Suppose you need to recognize which vehicle is a car and which one is a motorcycle.

In the supervised learning case, your input (training) dataset needs to be labelled, that is, for each input element in your input (training) dataset, you should specify if it represents a car or a motorcycle.

In the unsupervised learning case, you do not label the inputs. The unsupervised model clusters the input into clusters based e.g. on similar features/properties. So, in this case, there is are no labels like "car".

Answer 2

Supervised learning is when the data you feed your algorithm with is "tagged" or "labelled", to help your logic make decisions.

Example: Bayes spam filtering, where you have to flag an item as spam to refine the results.

Unsupervised learning are types of algorithms that try to find correlations without any external inputs other than the raw data.

Example: data mining clustering algorithms.

Answer 3

Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is.

Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: a hypothetical non-machine learning algorithm for face detection in images would try to define what a face is (round skin-like-colored disk, with dark area where you expect the eyes etc). A machine learning algorithm would not have such coded definition, but would "learn-by-examples": you'll show several images of faces and not-faces and a good algorithm will eventually learn and be able to predict whether or not an unseen image is a face.

This particular example of face detection is supervised, which means that your examples must be labeled, or explicitly say which ones are faces and which ones aren't.

In an unsupervised algorithm your examples are not labeled, i.e. you don't say anything. Of course, in such a case the algorithm itself cannot "invent" what a face is, but it can try to cluster the data into different groups, e.g. it can distinguish that faces are very different from landscapes, which are very different from horses.

Since another answer mentions it (though, in an incorrect way): there are "intermediate" forms of supervision, i.e. semi-supervised and active learning. Technically, these are supervised methods in which there is some "smart" way to avoid a large number of labeled examples. In active learning, the algorithm itself decides which thing you should label (e.g. it can be pretty sure about a landscape and a horse, but it might ask you to confirm if a gorilla is indeed the picture of a face). In semi-supervised learning, there are two different algorithms which start with the labeled examples, and then "tell" each other the way they think about some large number of unlabeled data. From this "discussion" they learn.

Answer 4

Machine learning: It explores the study and construction of algorithms that can learn from and make predictions on data.Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions expressed as outputs,rather than following strictly static program instructions.

Supervised learning: It is the machine learning task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.Specifically, a supervised learning algorithm takes a known set of input data and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new data.

Unsupervised learning: It is learning without a teacher. One basic thing that you might want to do with data is to visualize it. It is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning. Unsupervised learning uses procedures that attempt to find natural partitions of patterns.

With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you.Under the Unsupervised learning methods no labeled examples are provided and there is no notion of the output during the learning process. As a result, it is up to the learning scheme/model to find patterns or discover the groups of the input data

You should use unsupervised learning methods when you need a large amount of data to train your models, and the willingness and ability to experiment and explore, and of course a challenge that isn’t well solved via more-established methods.With unsupervised learning it is possible to learn larger and more complex models than with supervised learning.Here is a good example on it

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Answer 5

Supervised Learning: You give variously labelled example data as input, along with the correct answers. This algorithm will learn from it, and start predicting correct results based on the inputs thereafter. Example: Email Spam filter

Unsupervised Learning: You just give data and don't tell anything - like labels or correct answers. Algorithm automatically analyses patterns in the data. Example: Google News

Answer 6

I'll try to keep it simple.

Supervised Learning: In this technique of learning, we are given a data set and the system already knows the correct output of the data set. So here, our system learns by predicting a value of its own. Then, it does an accuracy check by using a cost function to check how close its prediction was to the actual output.

Unsupervised Learning: In this approach, we have little or no knowledge of what our result would be. So instead, we derive structure from the data where we don't know effect of variable. We make structure by clustering the data based on relationship among the variable in data. Here, we don't have a feedback based on our prediction.