supervised learning

Machine learning models and algorithms can be classified into three (3) major categories:

  • Supervised learning, i.e. a type of machine learning in which known label values are provided as input so that a model can estimate these values in future datasets. Examples of supervised learning algorithms are regression and classification algorithms, such as linear regression and logistic regression.
  • Semi-supervised learning, i.e. a type of machine learning in which some label values are provided as input, whereas the rest are unlabeled. A semi-supervised learning algorithm works in a very similar way as a supervised learning algorithm, after filling-in missing labels by using self-learning or other technique.
  • Unsupervised learning, i.e. a type of machine learning in which label values are not provided as input, so the model does not have an explicit variable that it is estimating. Examples of unsupervised learning algorithms are clustering algorithms, such as k-means clustering and hierarchical clustering (anglomerative and divisive).

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