Exploring supervised use cases and problem types

Supervised learning requireslabeled data. Labels, targets, and ground truth all refer to the same thing. The provided labels essentially supervise the learning process of the machine learning (ML) model and provide the feedback needed for a DL model to generate gradients and update itself. Labels can exist in many different forms. They are continuous numerical format, categorical format, text format, multiple categorical formats, image format, video format, audio format, and multiple target formats. All of these are then categorized as either of the following supervised problem types:

• Binary classification: This is when the target has categorical data with only two unique values.
• Multiclassification: This is when the target has categorical data with more than two unique values.
• Regression: This is when the target has continuous numerical data.
• Multi-target/problem:
  • Multilabel: This is when the target has more than...