Class Prediction Task
- AKA: Categorization, Categorical Label Assignment.
- input: an item (often a data item); and a class set.
- output: one or more class set members (possibly as Class Member Identifiers).
- measures: Accuracy.
- It can range from being a Binary Classification Task to being a Multiclass Classification Task (such as large multiclass classification), depending on the Class Set size.
- It can range from being a Unilabel Classification Task to being a Multilabel Classification Task, depending on the number of class outputs,.
- It can range from being a Simple-Input Classification Task to being a Complex-Input Classification Task, depending on the complexity of the input item.
- It can range from being a Model-based Classification Task to being ...
- It can range from being a Manual Classification Task to being an Automated Classification Task.
- It can range from being a Heuristic Classification Task to being a Data-Driven Classification Task (such as a supervised classification), depending on whether some uncertainty is allowed in the decision.
- It can range from being a Probabilistic Classification Task to being ...
- It can range from being a Batch Classification Task to being an Online Classification Task.
- It can be solved by a Classification System (that applies a classification algorithm).
- It can be instantiated as a Classification Act.
- It can (often) be a Data Classification Task.
- a Text Item Classification Task.
- an Image Classification Task.
- a Complex Input Classification Task, such as: Part-of-Speech Tagging, or Text Chunking.
- a Rules-based Classification Task, such as: Given the Classification Model IF X is human THEN X is mortal. what is the class membership of X=Socrates?
- a Biological Specific Classification.
- Given the Patient Record p, classify whether the patient should or should not be given aspirin.
- Guess the outcome of a Card Draw Experiment based on your intuition
- Guess the outcome of a Card Draw Experiment based on Data of the coin's past performance
- Guess whether Customer [math]X[/math] will not pay their current bill by its due date, based on your intuition.
- Guess whether Customer [math]X[/math] will not pay their current bill by its due date based on Data of the customer and other customer's past performance.
- See: Class, Cost-Benefit Matrix, Loss Function, Scientific Classification, Join Operation.
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Classification_(biology)
- Biological classification or scientific classification in biology, is a method by which biologists group and categorize species of organisms. Biological classification is a form of scientific taxonomy, but should be distinguished from folk taxonomy, which lacks scientific basis. ...
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Classification_(literature)
- Classification is a figure of speech linking a proper noun to a common noun using the or other articles.
- categorization: the act of distributing things into classes or categories of the same type
- a group of people or things arranged by class or category
- restriction imposed by the government on documents or weapons that are available only to certain authorized people
- The act of forming into a class or classes; a distribution into groups, as classes, orders, families, etc. ...
- arrange or order by classes or categories; "How would you classify these pottery shards--are they prehistoric?"
- declare unavailable, as for security reasons; "Classify these documents"
- relegate: assign to a class or kind; "How should algae be classified?"; "People argue about how to relegate certain mushrooms"
- to identify by or divide into classes; to categorize; to declare something a secret, especially a government secret
- (Hand, 2009) ⇒ David J. Hand. (2009). “Mismatched Models, Wrong Results, and Dreadful Decisions: On Choosing Appropriate Data Mining Tools.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557021
- (Quinlan, 1986a) ⇒ J. Ross Quinlan. (1986). “Induction of Tecision Trees." Machine Learning, 1(1).