Supervised vsUnsupervised Learning

Supervised and Unsupervised Learning categorizes the dataset into two where there are situations where sometimes we know or sometimes, we don’t know what’s in there.

We have discussed this on another topic i.e., Data Classification, Categorization and Data Clustering but as Supervised and Unsupervised Learning are very important to understand Machine Learning, let’s understand it again here.

In Machine Learning, there are Supervised and Unsupervised Learning methods.

Both terms are used for pattern identification before complex algorithms come into action.

Supervised Learning is done based on the uniqueness of the data where e.g., all animals are tagged separately. Whereas Unsupervised Learning is done based on the characteristics we can create clusters e.g., 1) Animals with 2 legs 2) Animals with 4 legs.

• Supervised Learning uses labeled data whereas Unsupervised Learning uses unlabeled data.
• The most popular Supervised Learning algorithms in data mining are the K-Nearest Neighbor and decision tree algorithms.
• The two common Unsupervised Learning algorithms in data mining are K-means clustering and hierarchical clustering.
• Supervised Learning output is known.
• Unsupervised Learning out is unknown.
• Training data is provided for Supervised Learning.
• No training data is provided for Unsupervised Learning.