Basics of an Classifier system in Machine learning

The training data set consists of Features and Labels

Data set = Training instances

Each Training Instance has multiple Attributes, which are also called “Features or Independent variables”.

Each Training Instance has single Label, which is called “Endpoint or Class or Dependent variable”.

Classifier system does Incremental learning. The Classifier takes one instance & learns/updates its pattern from Individual Training Instance.

Classifier will create “Prediction Model”[Regression/Classification], after all the Training instances are Processed.

The final Model = List of Conditions and Action for each Condition.

Rule = Condition -> Action