This section describes main characteristics of the monk2 data set and its attributes:
General information
MONK 2 data set 
Type  Classification  Origin  Laboratory 
Features  6  (Real / Integer / Nominal)  (0 / 6 / 0) 
Instances  432 
Classes  2 
Missing values?  No 
Attribute description
Attribute  Domain 
A1  [1, 3] 
A2  [1, 3] 
A3  [1, 2] 
A4  [1, 3] 
A5  [1, 4] 
A6  [1, 2] 
Class  {0,1} 
Additional information
The MONK's problems are a collection of three binary artificial classification problems (MONK1, MONK2 and MONK3) over a sixattribute discrete domain. Each problem involves learning a binary function defined over this domain, from a sample of training examples that belong to class 0 or class 1.
This is the second problem of the collection, with no random noise added.
In this section you can download some files related to the monk2 data set:
 The complete data set already formatted in KEEL format can be downloaded from
here.
 A copy of the data set already partitioned by means of a 10folds cross validation procedure can be downloaded from here.
 A copy of the data set already partitioned by means of a 5folds cross validation procedure can be downloaded from here.
 The header file associated to this data set can be downloaded from here.
 This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/MONK%27s+Problems.
