Supervised and Unsupervised learning

Supervised learning

Supervised learning is a machine learning technique. Supervised learning algorithms use labeled data (i.e., data tagged with the correct outcome) to predict outcomes for unseen data.

A supervised learning algorithm analyzes the training data and produces an inferred function that is then used for mapping new examples. This allows the algorithm to correctly determine the class labels for unseen instances.

There are two types of supervised learning algorithms:

• Classification: This technique is used to predict the category of a variable.
• Regression: This technique is used to predict numerical values.

Unsupervised learning

Unsupervised learning is a machine learning technique that is used to find previously unknown patterns in data. Unsupervised learning algorithms use data without labeled outcomes to predict outcomes for unseen data.

In unsupervised learning, unsorted information is grouped according to similarities and differences even though no categories are provided. There are two main types of unsupervised learning algorithms:

• Clustering: Clustering algorithms process data to split data points into clusters with similar features.
• Association: Association learning involves finding relationships between features in a dataset based on significance levels to filter the interesting rules.

Difference

The main difference between these two learning algorithms are:

• Input data: In a supervised learning model, input and output variables will be given whereas, in an unsupervised learning model, only input data will be given.

• Complexity: Supervised learning is simpler than unsupervised learning which requires a huge computational power to process.

• Accuracy of results: Supervised learning has more accurate results than unsupervised learning.

• Real-time learning: Supervised learning takes place offline, whereas unsupervised learning takes place online to process data in real-time.