Supervised vs Unsupervised Machine Learning


Machine learning is an exciting field that can be used to solve problems, but it’s not always clear which type of machine learning a problem calls for. That’s why in this post we’ll discuss the differences between supervised and unsupervised machine learning.

Supervised Learning

Supervised learning is a machine learning technique in which the algorithm is trained with labeled examples, making it capable of predicting the output for a new input. This type of machine learning is used to predict an output variable from a set of input variables.

For example: If you have data about how much money spent on ads, what time they were posted and so on… You can use supervised learning algorithms like logistic regression or support vector machines (SVMs) to predict whether those ads will be clicked or not based on this information.

Unsupervised Learning

Unsupervised Learning

Unsupervised learning is a machine learning technique that helps you discover hidden patterns in data. It’s useful for data mining, predictive modeling and time series prediction. Unsupervised learning is also known as “self-organizing” because it allows you to determine what structure exists within your data set without having any prior knowledge of the type of data or structure that might be present in it.

With supervised learning, the input data is correlated with an output variable. With unsupervised learning, the input data is not correlated with any output variable.

Supervised learning is an approach to machine learning that uses labeled examples to train a model. The data used in supervised learning consists of both input variables and output variables; these are called independent and dependent variables respectively. For example, let’s say you’re trying to predict whether someone will survive cancer or not based on their age and gender. In this case, age would be an input variable while survival status (1 if they survived cancer; 0 otherwise) would be your output variable–you could then classify people into different groups based on their survival status: those who lived through cancer belonged in one group while those who died from it belonged in another group; these two groups might look like this:

You’ll notice that there are two types of supervised machine learning algorithms: regression models (which aim at predicting continuous values like sales volume) and classification models (which aim at classifying items into discrete categories such as spam vs non-spam). With both types of algorithms, we must first specify which features we want them to learn from our dataset – these features could be anything from price fluctuations over time for stocks listed at NYSE exchange markets up until January 1st 2020 or even more complex things like genetic markers associated with certain diseases like Cystic Fibrosis which cause severe lung infections among other symptoms related specifically only


In general, supervised learning is more useful than unsupervised learning. Supervised learning allows you to predict future outcomes based on past data, while unsupervised learning does not provide this capability. However, unsupervised learning can still be useful for analyzing patterns or relationships within your data set without needing specific answers ahead of time.