Data-Driven Classification Task
A Data-Driven Classification Task is a classification task that is a data-driven prediction task.
- Context:
- It can be solved by a Data-Driven Classification System (that implements a Data-Driven Classification algorithm).
- It can range from being a Supervised Classification Task (such as fully-supervised classification) to being an Unsupervised Classification Task.
- It can range from being a Univariate Data-Driven Classification Task to being a Multivariate Data-Driven Classification Task, depending on the feature set size.
- It can range from being a Data-Driven One-Label Classification Task to being a Data-Driven Multi-Label Classification Task, depending on the number of class labels to predict.
- It can range from being an Online Data-Driven Classification Task to being an Offline Data-Driven Classification Task, depending on the incremental availability of data.
- It can range from being a IID Data-Driven Classification Task to being an Non-IID Data-Driven Classification Task (such as [[temporal data-driven classification)), depending on whether it receives IID data).
- It can range from being a Model-based Data-Driven Classification Task to being an Model-free Data-Driven Classification Task (such as [[instanced-based classification), depending on the requirement of a classification model.
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- Example(s):
- Counter-Example(s):
- See: Concept Learning, Data-Driven Record Linkage.