Explain Supervised vs. Unsupervised Learning in Machine Learning to a 5-year-old
By: Parthvi Shah
Tags: Basics, Data Science, Supervised Learning, Machine Learning, Unsupervised Learning

Explain Supervised vs. Unsupervised Learning in Machine Learning to a 5-year-old

Machine Learning

With the help of Machine learning, a system can make decision which can be relatable to the decisions that humans make. Machine learning has the ability to learn from the data it inputs. For now, Machine learning is trying to imitate the way humans learn in a computationally efficient manner.

Supervised Learning

Supervised learning contains of a dataset with a set of features and the corresponding target label. These sets of features are also known as predictors because they help in predicting the target label based on each data sample.

For example: Imagine giving a kid an ice-cream whenever he finishes his meal, eats a fruit with his meal, finishes his homework and also helps in cleaning at home. However, whenever the kid watches TV for a long time, only plays with his friends without finishing his homework or already eats a chocolate with his meal, he is definitely not getting an ice-cream. He will get a vegetable for snack instead. Here, ice-creams, carrots and green veggies are the target variables.

supervised learning [last column is the target variable] (image created by author)

There are various kinds of techniques in supervised learning:

Classification Techniques

As the name suggests, whenever we have a category to classify, we use classification techniques. For example: ice-cream, carrots and green-veggies are our classes.

2. Regression Techniques

If the label is anything other than a category, we use regression techniques. For example: If we had to predict how many carrots or how many ice-creams would the kid get every time he follows a certain pattern, we would use a regression technique instead.

Supervised learning techniques are used widely for predicting a future value. They can be used to predict the weather, to predict if someone will pass/fail the class, etc.

Unsupervised Learning

As the name suggests, unsupervised learning is not supervised and we only have a list of input variables and no target label. The main aim of this type of algorithm is to learn the underlying distribution in the data and gather data which might be similar to each other.

example: clustering [unsupervised learning] (image created by author)

For a kid to understand unsupervised learning, we can show the above image to a kid, and ask them to circle an odd member [just like circle the odd man out] from each line. More often than not, they will circle images which are not ice-creams. This is a type of clustering technique. We as humans, are collecting and memorizing objects which seem similar to us. This is what we expect our model to do, too.

Clustering is a type of unsupervised technique where we try to gather information into various groups and each group has data points which are most similar to each other than data point in any other group.

Unsupervised machine learning helps you to finds all kind of unknown patterns in data. Unsupervised learning techniques are used widely in recommendation systems which use clustering techniques to find similar users by the content they see, etc.