View a1a (public)
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(No information yet)
- License
- unknown (from LibSVMTools repository)
- Dependencies
- Tags
- libsvm LibSVMTools slurped
- Attribute Types
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# Instances: 65122 / # Attributes: 124
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- Original Data Format
- libsvm
- Name
- a1a
- Version mldata
- 0
- Comment
LibSVM
- Names
- Data (first 10 data points)
-1 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -1 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -1 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 ... -1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 ... -1 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 ... -1 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 ... -1 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 ... -1 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 ... -1 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 ... 1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 ... ... ... ... ... ... ... ... ... ... ... ...
- Description
LIBSVM Data: Classification (Binary Class)LIBSVM Data: Classification (Binary Class) This page contains many classification, regression, and multi-label data sets used in our papers. Many are from UCI, Statlog, StatLib and other collections. We really thank their efforts. For most sets, we directly transform the file into LIBSVM format and linearly scale each attribute to [-1,1]. The testing data (if provided) is adjusted accordingly. Some training data are further separated to "training" (tr) and "validation" (val) sets. Details can be found in the description of each data set. Preprocessing: The original Adult data set has 14 features, among which six are continuous and eight are categorical. In this data set, continuous features are discretized into quantiles, and each quantile is represented by a binary feature. Also, a categorical feature with m categories is converted to m binary features. Details on how each feature is converted can be found in the beginning of each file from this page. [JP98a] # of classes: 2# of data: 1,605 / 30,956 (testing) # of features: 123 / 123 (testing)
- URLs
- (No information yet)
- Publications
- Data Source
- http://www.ics.uci.edu/~mlearn/MLRepository.html UCI / Adult
- Measurement Details
- Usage Scenario
- revision 1
- by mldata on 2010-11-01 11:36
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Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.