Machine learning models can be divided into two broad groups depending on whether our data contains labels or not. Supervised models require labelled data whereas unsupervised models do not necessarily need labels.
Supervised models are typically used to predict some target attrbute based on observed values of predictor attributes. If the target that we want to calculate is continuous then we have a regression problem. On the other hand, if we are dealing with a discrete target, then it is called a classification problem.
- Linear Regression
- Logistic Regression
- k-Nearest Neighbours (kNN)
- Decision Trees and Random Forests
- Support Vector Machines
For descriptive tasks where do not have a designated target attribute, we can use unsupervised models. Instead of predicting something, our goal is to learn from the data and the process that generated the data. Our task is to identify coherent subgroups in the data.
- k-Means
- Hierarchical Clustering
- DBSCAN