- All data is labeled and the algorithms learn to predict the output from the input data.
- Classification , Regression
- Linear regression and classification , Random forest , Support vector machine , Bayesian, Neural networks
- All data is unlabeled and the algorithms learn to inherent structure from the input data.
- Clusterring, Association
- K-means , Apriori Algorithm
- Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used.
- Many real world machine learning problems fall into this area. This is because it can be expensive or time-consuming to label data as it may require access to domain experts. Whereas unlabeled data is cheap and easy to collect and store.
- You can use unsupervised learning techniques to discover and learn the structure in the input variables.
- You can also use supervised learning techniques to make best guess predictions for the unlabeled data, feed that data back into the supervised learning algorithm as training data and use the model to make predictions on new unseen data.