![]() It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1).Ī11. ![]() The rows represent the actual classification and the columns the predicted classification. This dataset requires use of a cost matrix (see below) Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. ![]() Hofmann, contains categorical/symbolic attributes and is in the file "german.data".įor algorithms that need numerical attributes, Strathclyde University produced the file "german.data-numeric". the original dataset, in the form provided by Prof. Click here to try out the new site.ĭownload: Data Folder, Data Set DescriptionĪbstract: This dataset classifies people described by a set of attributes as good or bad credit risks. Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |