For supervised learning problems, the input to a learning problem is a dataset consisting of labeled data. By this, we mean that we have outputs whose values are known. The learning program is fed with input samples and their corresponding outputs and its goal is to decipher the relationship between them. Such input is known as labeled data. Supervised learning problems include the following:

• Classification: The learned attribute is categorical (nominal) or discrete
• Regression: The learned attribute is numeric/continuous

In unsupervised learning or data mining, the learning program is fed with inputs but does without the corresponding outputs. This input data is referred to as unlabeled data. The goal of machine learning in such cases is to learn or decipher the hidden label. Such problems include the following:

• Clustering
• Dimensionality reduction