Supervised and Unsupervised learning

Supervised vs Unsupervised Learning

In data science world supervised and unsupervised learning algorithms were the famous words we could hear more frequently when we were talking with the people who are working in data science field, furthermore the key differences between these two learning algorithms is the must learn concepts for differentiating the real world problems.

Supervised Learning Wiki Definition

        Supervised learning is a data mining 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 the 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. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a “reasonable” way.

Unsupervised Learning Wiki Definition

In data mining or even in data science world, the problem of an unsupervised learning  task is trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution.

Supervised  and unsupervised learning with a real-life example

Examples for supervised and unsupervised classification

  • Suppose you have a basket and it is filled with different kinds of fruits.
  • Your task is to arrange them as groups.
  • For understanding let me clear the names of the fruits in our basket.
  • We have four types of fruits. They are
red apple






cherries supervised learning examle



Supervised Learning :

  • You already learn from your previous work about the physical characters of fruits
  • So arranging  the same type of fruits at one place is easy now
  • In data mining terminology the previous work is called as training the data
  • You already learn the things from your train data, this is because of response variable
  • Response variable means just a decision variable
  • You can observe response variable below (FRUIT NAME)
1 Big Red Rounded shape with a depression at the top Apple
2 Small Red Heart-shaped to nearly globular Cherry
3 Big Green Long curving cylinder Banana
4 Small Green Round to oval,Bunch shape Cylindrical Grape
  • Suppose you have  taken a new fruit from the basket then you will see the size , color, and shape of that particular fruit.
  • If  size  is Big , color is Red , the shape is rounded shape with a depression at the top, you will confirm the fruit name as apple and you will put in apple group.
  • Likewise for other fruits also.
  • The job of grouping fruits was done and the happy ending.
  • You can observe in the table that  a column was labeled as “FRUIT NAME“. This is called as a response variable.
  • If you learn the thing before from training data and then applying that knowledge to the test data(for new fruit), This type of learning is called as Supervised Learning.

Supervised Learning Algorithms:

All classification and regression algorithms come under supervised learning.

  • Logistic Regression
  • Decision trees
  • Support vector machine (SVM)
  • k-Nearest Neighbors
  • Naive Bayes
  • Random forest
  • Linear regression
  • polynomial regression
  • SVM for regression

Unsupervised Learning :

  • Suppose you have a basket and it is filled with some different types of fruits and your task is to arrange them as groups.
  • This time, you don’t know anything about the fruits, honestly saying this is the first time you have seen them. You have no clue about those.
  • So, how will you arrange them?
  • What will you do first???
  • You will take a fruit and you will arrange them by considering the physical character of that particular fruit.
  • Suppose you have considered color.
    • Then you will arrange them on considering base condition as color.
    • Then the groups will be something like this.
      • RED COLOR GROUP: apples & cherry fruits.
      • GREEN COLOR GROUP: bananas & grapes.
  • So now you will take another physical character such as size .
    • RED COLOR AND BIG SIZE: apple.
    • RED COLOR AND SMALL SIZE: cherry fruits.
    • GREEN COLOR AND BIG SIZE: bananas.
  • The job has done, the happy ending.
  • Here you did not learn anything before ,means no train data and no response variable.
  • In data mining or machine learning, this kind of learning is known as unsupervised learning.

Unsupervised learning algorithms:

All clustering algorithms come under unsupervised learning algorithms.

  • K – means clustering
  • Hierarchical clustering
  • Hidden Markov models


Let’s summarize what we have learnt in supervised and unsupervised learning algorithms post.

Supervised learning: Learning from the know label data to create a model then predicting target class for the given input data.

Unsupervised learning: Learning from the unlabeled data to differentiating the given input data.


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