What is the difference between supervised learning and unsupervised learning

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In terms of artificial intelligence and machine learning  what is the difference between supervised and unsupervised learning  Can you provide a basic  easy explanation with an example

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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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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

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Supervised learning  A supervised learning algorithm analyzes the training data and produces an inferred function  which can be used for mapping new examples   We provide training data and we know correct output for a certain input We know relation between input and output  Categories of problem  Regression   Predict results within a continuous output   gt  map input variables to some continuous function  Example  Given a picture of a person  predict his age Classification  Predict results in a discrete output   gt   map input variables into discrete categories Example  Is this tumer cancerous   Unsupervised learning  Unsupervised learning learns from test data that has not been labeled  classified or categorized  Unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data   We can derive this structure by clustering the data based on relationships among the variables in the data   There is no feedback based on the prediction results    Categories of problem  Clustering  is the task of grouping a set of objects in such a way that objects in the same group  called a cluster  are more similar  in some sense  to each other than to those in other groups  clusters  Example  Take a collection of 1 000 000 different genes  and find a way to automatically group these genes into groups that are somehow similar or related by different variables  such as lifespan  location  roles  and so on   Popular use cases are listed here  Difference between classification and clustering in data mining  References  Supervised learning Unsupervised learning machine-learning from coursera towardsdatascience

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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

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For instance  very often training a neural network is supervised learning  you re telling the network to which class corresponds the feature vector you re feeding   Clustering is unsupervised learning  you let the algorithm decide how to group samples into classes that share common properties   Another example of unsupervised learning is Kohonen s self organizing maps

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Supervised Learning     In this  every input pattern that is used to train the network is   associated with an output pattern  which is the target or the desired   pattern  A teacher is assumed to be present during the learning   process  when a comparison is made between the network s computed   output and the correct expected output  to determine the error  The   error can then be used to change network parameters  which result in   an improvement in performance    Unsupervised Learning     In this learning method  the target output is not presented to the   network  It is as if there is no teacher to present the desired   pattern and hence  the system learns of its own by discovering and   adapting to structural features in the input patterns

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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

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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

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In simple words      It s my understanding  feel free to correct  Supervised learning is  we know what we are predicting on the basis of provided data  So we have a column in the dataset which needs to be predicated  Unsupervised learning is  we try to extract meaning out of the provided dataset  We don t have clarity on what to be predicted  So question is why we do this       Answer is - the outcome of Unsupervised learning is groups clusters similar data together   So if we receive any new data then we associate that with the identified cluster group and understand it s features   I hope it will help you

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In supervised learning  the input x is provided with the expected outcome y  i e   the output the model is supposed to produce when the input is x   which is often called the  class   or  label   of the corresponding input x   In unsupervised learning  the  class  of an example x is not provided  So  unsupervised learning can be thought of as finding  hidden structure  in unlabelled data set    Approaches to supervised learning include    Classification  1R  Naive Bayes  decision tree learning algorithm  such as ID3 CART  and so on  Numeric Value Prediction   Approaches to unsupervised learning include    Clustering  K-means  hierarchical clustering  Association Rule Learning

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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

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Supervised Machine Learning       The process of  an algorithm learning from  training dataset and   predict the  output      Accuracy of  predicted output  directly proportional   to the  training data  length   Supervised learning is where you have input variables  x   training dataset  and an output variable  Y   testing dataset  and you use an algorithm to learn the mapping function from the input to the output   Y   f X    Major types     Classification  discrete y-axis   Predictive  continuous y-axis    Algorithms     Classification Algorithms   Neural Networks   Na  ve Bayes classifiers   Fisher linear discriminant   KNN  Decision Tree  Super Vector Machines  Predictive Algorithms   Nearest  neighbor   Linear Regression Multi Regression     Application  areas     Classifying  emails as spam    Classifying whether patient has disease or not    Voice  Recognition  Predict  the HR select particular candidate or  not  Predict the stock market price

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Supervised learning  say a kid goes to kinder-garden  here teacher shows him 3 toys-house ball and car  now teacher gives him 10 toys   he will classify them  in 3 box of house ball and car based on his previous experience  so kid was first supervised by teachers for getting right answers for few sets  then he was tested on unknown toys    Unsupervised learning  again kindergarten example A child is given 10 toys  he is told to segment similar ones  so based on features like shape size color function etc he will try to make 3 groups say A B C and group them    The word Supervise means you are giving supervision instruction to machine to help it find answers  Once it learns instructions  it can easily predict for new case   Unsupervised  means there is no supervision or instruction how to find answers labels and machine will use its intelligence to find some pattern in our data  Here it will not make prediction  it will just try to find clusters which has similar data

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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

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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

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Supervised Learning        Unsupervised Learning        Example    Supervised Learning    One bag with apple   One bag with orange     build model One mixed bag of apple and orange      Please classify    Unsupervised Learning     One mixed bag of apple and orange      build model  Another mixed bag     Please classify

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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

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In Simple       Supervised learning is  type of machine learning problem in which we have some labels and by using that labels we implement algorithm such as regression and classification  Classification is applied where our output is like in the form of  0 or 1  true false yes no  and regression is applied where out put a real value such a house of price   Unsupervised Learning is a type of machine learning problem in which we don t have any labels means we have some data only  unstructured data and we have to cluster the data  grouping of data using various unsupervised algorithm

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Supervised Learning is basically where you have input variables x  and output variable y  and use algorithm to learn the mapping function from input to the output  The reason why we called this as supervised is because algorithm learns from the training dataset  the algorithm iteratively makes predictions on the training data  Supervised have two types-Classification and Regression  Classification is when the output variable is category like yes no  true false  Regression is when the output is real values like height of person  Temperature etc   UN supervised learning is where we have only input data X  and no output variables  This is called an unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher  Algorithms are left to their own devises to discover and present the interesting structure in the data   Types of unsupervised learning are clustering and Association

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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

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Machine learning is a field where you are trying to make machine to mimic the human behavior   You train machine just like a baby The way humans learn  identify features  recognize patterns and train himself  same way you train machine by feeding data with various features  Machine algorithm identify the pattern within the data and classify it into particular category   Machine learning broadly divided into two category  supervised and unsupervised learning   Supervised learning is the concept where you have input vector   data with corresponding target value  output  On the other hand unsupervised learning is the concept where you only have input vectors   data without any corresponding target value   An example of supervised learning is handwritten digits recognition where you have image of digits with corresponding digit  0-9   and an example of unsupervised learning is grouping customers by purchasing behavior

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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

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Supervised Learning  Supervised learning is based on training a data sample from data source with correct classification already assigned  Such techniques are utilized in feedforward or MultiLayer Perceptron  MLP  models  These MLP has three distinctive characteristics    One or more layers of hidden neurons that are not part of the input or output layers of the network that enable the network to learn and solve any complex problems The nonlinearity reflected in the neuronal activity is differentiable and  The interconnection model of the network exhibits a high degree of connectivity    These characteristics along with learning through training solve difficult and diverse problems  Learning through training in a supervised ANN model also called as error backpropagation algorithm  The error correction-learning algorithm trains the network based on the input-output samples and finds error signal  which is the difference of the output calculated and the desired output and adjusts the synaptic weights of the neurons that is proportional to the product of the error signal and the input instance of the synaptic weight  Based on this principle  error back propagation learning occurs in two passes   Forward Pass    Here  input vector is presented to the network  This input signal propagates forward  neuron by neuron through the network and emerges at the output end of the network as output signal  y n    f v n   where v n  is the induced local field of a neuron defined by v n   S w n y n   The output that is calculated at the output layer o n  is compared with the desired response d n  and finds the error e n  for that neuron  The synaptic weights of the network during this pass are remains same   Backward Pass    The error signal that is originated at the output neuron of that layer is propagated backward through network  This calculates the local gradient for each neuron in each layer and allows the synaptic weights of the network to undergo changes in accordance with the delta rule as    w n        d n    y n     This recursive computation is continued  with forward pass followed by the backward pass for each input pattern till the network is converged   Supervised learning paradigm of an ANN is efficient and finds solutions to several linear and non-linear problems such as classification  plant control  forecasting  prediction  robotics etc   Unsupervised Learning  Self-Organizing neural networks learn using unsupervised learning algorithm to identify hidden patterns in unlabelled input data  This unsupervised refers to the ability to learn and organize information without providing an error signal to evaluate the potential solution  The lack of direction for the learning algorithm in unsupervised learning can sometime be advantageous  since it lets the algorithm to look back for patterns that have not been previously considered  The main characteristics of Self-Organizing Maps  SOM  are    It transforms an incoming signal pattern of arbitrary dimension into one or 2 dimensional map and perform this transformation adaptively The network represents feedforward structure with a single computational layer consisting of neurons arranged in rows and columns  At each stage of representation  each input signal is kept in its proper context and  Neurons dealing with closely related pieces of information are close together and they communicate through synaptic connections    The computational layer is also called as competitive layer since the neurons in the layer compete with each other to become active  Hence  this learning algorithm is called competitive algorithm  Unsupervised algorithm in SOM works in three phases   Competition phase   for each input pattern x  presented to the network  inner product with synaptic weight w is calculated and the neurons in the competitive layer finds a discriminant function that induce competition among the neurons and the synaptic weight vector that is close to the input vector in the Euclidean distance is announced as winner in the competition  That neuron is called best matching neuron   i e  x   arg min   x - w      Cooperative phase    the winning neuron determines the center of a topological neighborhood h of cooperating neurons  This is performed by the lateral interaction d among the cooperative neurons  This topological neighborhood reduces its size over a time period   Adaptive phase    enables the winning neuron and its neighborhood neurons to increase their individual values of the discriminant function in relation to the input pattern through suitable synaptic weight adjustments      w    h x  x    w     Upon repeated presentation of the training patterns  the synaptic weight vectors tend to follow the distribution of the input patterns due to the neighborhood updating and thus ANN learns without supervisor   Self-Organizing Model naturally represents the neuro-biological behavior  and hence is used in many real world applications such as clustering  speech recognition  texture segmentation  vector coding etc   Reference

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Supervised learning can label a new item into one of the trained labels based on learning during training  You need to provide large numbers of training data set  validation data set and test data set  If you provide say pixel image vectors of digits along with training data with labels  then it can identify the numbers     Unsupervised learning does not require training data-sets  In unsupervised learning it can group items into different clusters based on the difference in the input vectors  If you provide pixel image vectors of digits and ask it to classify into 10 categories  it may do that  But it does know how to labels it as you have not provided training labels

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There are many answers already which explain the differences in detail  I found these gifs on codeacademy and they often help me explain the differences effectively   Supervised Learning   Notice that the training images have labels here and that the model is learning the names of the images   Unsupervised Learning   Notice that what s being done here is just grouping clustering  and that the model doesn t know anything about any image

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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

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Supervised Learning  You have labeled data and have to learn from that  e g house data along with price and then learn to predict price  Unsupervised learning  you have to find the trend and then predict  no prior labels given  e g different people in the class and then a new person comes so what group does this new student belong to

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Supervised learning  Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems   Unsupervised learning  In other pattern recognition problems  the training data consists of a set of input vectors x without any corresponding target values  The goal in such unsupervised learning problems may be to discover groups of similar examples within the data  where it is called clustering  Pattern Recognition and Machine Learning  Bishop  2006

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

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