Supervised learning When working with labeled data. Depending on the target type can be either classification (discrete values) or regression (continuous values)
Supervised learning When working with labeled data. Depending on the target type can be either classification (discrete values) or regression (continuous values)
Single/multi output Single-output methods predict a single target-label (binary or multi-class) for classification or a single target-value for regression. Multi-output methods simultaneously predict multiple variables given an input.
Concept drift detection Changes in data distribution can harm learning. Drift detection methods are designed to rise an alarm in the presence of drift and are used alongside learning methods to improve their robustness against this phenomenon in evolving data streams.
Unsupervised learning When working with unlabeled data. For example, anomaly detection where the goal is the identification of rare events or samples which differ significantly from the majority of the data.
Montiel, J., Read, J., Bifet, A., & Abdessalem, T. (2018). Scikit-multiflow: A multi-output streaming framework. The Journal of Machine Learning Research, 19(72):1−5.