TABLE OF CONTENTS Oded Maimon
Department of Industrial Engineering
Tel-Aviv University
maimon @ eng.tau.ac.il
Lior Rokach
Department of Industrial Engineering
Tel-Aviv University
liorr@eng.tau.ac.il
| Abstract | This chapter summarizes the fundamental aspects of supervised methods. The chapter provides an overview of concepts from various interrelated fields used in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks. |
| Keywords: | Attribute, Classifier, Inducer, Regression, Training Set, Supervised Methods, Instance Space, Sampling, Generalization Error |
Supervised methods are methods that attempt to discover the relationship between input attributes (sometimes called independent variables) and a target attribute (sometimes referred to as a dependent variable). The relationship discovered is represented in a structure referred to as a model. Usually models describe and explain phenomena, which are hidden in the dataset and can be used for predicting the value of the target attribute knowing the values of the input attributes. The supervised methods can be implemented in a variety of domains such as marketing, finance and manufacturing.
It is useful to distinguish between two main supervised models: classification models ( classifiers) and Regression Models. Regression models map the input space into a real-value domain. For instance, a regressor can predict the demand for...
