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

Answer 1

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.

Answer 2

The learning algorithm of a neural network can either be supervised or unsupervised.

A neural net is said to learn supervised if the desired output is already known. Example: pattern association

Neural nets that learn unsupervised have no such target outputs. It can't be determined what the result of the learning process will look like. During the learning process, the units (weight values) of such a neural net are "arranged" inside a certain range, depending on given input values. The goal is to group similar units close together in certain areas of the value range. Example: pattern classification

Answer 3

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.

Answer 4

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  
  Naïve Bayes classifiers  
  Fisher linear discriminant  
  KNN 
  Decision Tree 
  Super Vector Machines
  

• Predictive Algorithms:

  Nearest  neighbor  
  Linear Regression,Multi Regression 
  

Application areas:

1. Classifying emails as spam
2. Classifying whether patient has disease or not

3. Voice Recognition

4. Predict the HR select particular candidate or not

5. Predict the stock market price

Answer 5

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)

Answer 6

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.