# Adult Income Data Set

## Classify Adult Income

**Source:**

Donor:

Ronny Kohavi and Barry Becker

Data Mining and Visualization

Silicon Graphics.

e-mail: ronnyk '@' live.com for questions.

**Data Set Information:**

Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))

Prediction task is to determine whether a person makes over 50K a year.

**Relevant Papers:**

Ron Kohavi, "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996

[Web Link]

**Papers That Cite This Data Set ^{1}:**

Rakesh Agrawal and Ramakrishnan ikant and Dilys Thomas. Privacy Preserving OLAP. SIGMOD Conference. 2005. [View Context].

Rich Caruana and Alexandru Niculescu-Mizil. An Empirical Evaluation of Supervised Learning for ROC Area. ROCAI. 2004. [View Context].

Rich Caruana and Alexandru Niculescu-Mizil and Geoff Crew and Alex Ksikes. Ensemble selection from libraries of models. ICML. 2004. [View Context].

Bianca Zadrozny. Learning and evaluating classifiers under sample selection bias. ICML. 2004. [View Context].

Wei-Chun Kao and Kai-Min Chung and Lucas Assun and Chih-Jen Lin. Decomposition Methods for Linear Support Vector Machines. Neural Computation, 16. 2004. [View Context].

Saharon Rosset. Model selection via the AUC. ICML. 2004. [View Context].

Alexander J. Smola and Vishy Vishwanathan and Eleazar Eskin. Laplace Propagation. NIPS. 2003. [View Context].

I. Yoncaci. Maximum a Posteriori Tree Augmented Naive Bayes Classifiers. O EN INTEL.LIG ` ENCIA ARTIFICIAL CSIC. 2003. [View Context].

Christopher R. Palmer and Christos Faloutsos. Electricity Based External Similarity of Categorical Attributes. PAKDD. 2003. [View Context].

S. Sathiya Keerthi and Chih-Jen Lin. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation, 15. 2003. [View Context].

Thomas Serafini and G. Zanghirati and Del Zanna and T. Serafini and Gaetano Zanghirati and Luca Zanni. DIPARTIMENTO DI MATEMATICA. Gradient Projection Methods for. 2003. [View Context].

Bart Hamers and J. A. K Suykens. Coupled Transductive Ensemble Learning of Kernel Models. Bart De Moor. 2003. [View Context].

Andrew W. Moore and Weng-Keen Wong. Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning. ICML. 2003. [View Context].

Ramesh Natarajan and Edwin P D Pednault. Segmented Regression Estimators for Massive Data Sets. SDM. 2002. [View Context].

Bianca Zadrozny and Charles Elkan. Transforming classifier scores into accurate multiclass probability estimates. KDD. 2002. [View Context].

Nitesh V. Chawla and Kevin W. Bowyer and Lawrence O. Hall and W. Philip Kegelmeyer. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. (JAIR, 16. 2002. [View Context].

S. Sathiya Keerthi and Kaibo Duan and Shirish Krishnaj Shevade and Aun Neow Poo. A Fast Dual Algorithm for Kernel Logistic Regression. ICML. 2002. [View Context].

Stephen D. Bay and Michael J. Pazzani. Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov, 5. 2001. [View Context].

Jie Cheng and Russell Greiner. Learning Bayesian Belief Network Classifiers: Algorithms and System. Canadian Conference on AI. 2001. [View Context].

Zhiyuan Chen and Johannes Gehrke and Flip Korn. Query Optimization In Compressed Database Systems. SIGMOD Conference. 2001. [View Context].

Stephen D. Bay. Multivariate Discretization for Set Mining. Knowl. Inf. Syst, 3. 2001. [View Context].

Bernhard Pfahringer and Geoffrey Holmes and Richard Kirkby. Optimizing the Induction of Alternating Decision Trees. PAKDD. 2001. [View Context].

Dmitry Pavlov and Jianchang Mao and Byron Dom. Scaling-Up Support Vector Machines Using Boosting Algorithm. ICPR. 2000. [View Context].

Gary M. Weiss and Haym Hirsh. A Quantitative Study of Small Disjuncts: Experiments and Results. Department of Computer Science Rutgers University. 2000. [View Context].

Dmitry Pavlov and Darya Chudova and Padhraic Smyth. Towards scalable support vector machines using squashing. KDD. 2000. [View Context].

Kristin P. Bennett and Ayhan Demiriz and John Shawe-Taylor. A Column Generation Algorithm For Boosting. ICML. 2000. [View Context].

Jie Cheng and Russell Greiner. Comparing Bayesian Network Classifiers. UAI. 1999. [View Context].

Petri Kontkanen and Jussi Lahtinen and Petri Myllymaki and Tomi Silander and Henry Tirri. Proceedings of Pre- and Post-processing in Machine Learning and Data Mining: Theoretical Aspects and Applications, a workshop within Machine Learning and Applications. Complex Systems Computation Group (CoSCo). 1999. [View Context].

Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998. [View Context].

John C. Platt. Using Analytic QP and Sparseness to Speed Training of Support Vector Machines. NIPS. 1998. [View Context].

Ron Kohavi. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. KDD. 1996. [View Context].

Shi Zhong and Weiyu Tang and Taghi M. Khoshgoftaar. Boosted Noise Filters for Identifying Mislabeled Data. Department of Computer Science and Engineering Florida Atlantic University. [View Context].

David R. Musicant. DATA MINING VIA MATHEMATICAL PROGRAMMING AND MACHINE LEARNING. Doctor of Philosophy (Computer Sciences) UNIVERSITY. [View Context].

William W. Cohen and Yoram Singer. A Simple, Fast, and Effective Rule Learner. AT&T Labs--Research Shannon Laboratory. [View Context].

Haixun Wang and Philip S. Yu. SSDT-NN: A Subspace-Splitting Decision Tree Classifier with Application to Target Selection. IBM T. J. Watson Research Center. [View Context].

S. V. N Vishwanathan and Alexander J. Smola and M. Narasimha Murty. considerably faster than competing methods such as Sequential Minimal Optimization or the Nearest Point Algorithm. Machine Learning Program, National ICT for Australia. [View Context].

Grigorios Tsoumakas and Ioannis P. Vlahavas. Fuzzy Meta-Learning: Preliminary Results. Greek Secretariat for Research and Technology. [View Context].

Josep Roure Alcobe. Incremental Hill-Climbing Search Applied to Bayesian Network Structure Learning. Escola Universitria Politcnica de Mataro. [View Context].

Ayhan Demiriz and Kristin P. Bennett and John Shawe and I. Nouretdinov V.. Linear Programming Boosting via Column Generation. Dept. of Decision Sciences and Eng. Systems, Rensselaer Polytechnic Institute. [View Context].

Chris Giannella and Bassem Sayrafi. An Information Theoretic Histogram for Single Dimensional Selectivity Estimation. Department of Computer Science, Indiana University Bloomington. [View Context].

Rong-En Fan and P. -H Chen and C. -J Lin. Working Set Selection Using the Second Order Information for Training SVM. Department of Computer Science and Information Engineering National Taiwan University. [View Context].

Petri Kontkanen and Jussi Lahtinen and Petri Myllymaki and Tomi Silander and Henry Tirri. USING BAYESIAN NETWORKS FOR VISUALIZING HIGH-DIMENSIONAL DATA. Complex Systems Computation Group (CoSCo). [View Context].

Ahmed Hussain Khan and Intensive Care. Multiplier-Free Feedforward Networks. 174. [View Context].

Luc Hoegaerts and J. A. K Suykens and J. Vandewalle and Bart De Moor. Subset Based Least Squares Subspace Regression in RKHS. Katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SCD-SISTA. [View Context].

David R. Musicant and Alexander Feinberg. Active Set Support Vector Regression. [View Context].

Luc Hoegaerts and J. A. K Suykens and J. Vandewalle and Bart De Moor. Primal Space Sparse Kernel Partial Least Squares Regression for Large Scale Problems Special Session paper . Katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SCD-SISTA. [View Context].

Kuan-ming Lin and Chih-Jen Lin. A Study on Reduced Support Vector Machines. Department of Computer Science and Information Engineering National Taiwan University. [View Context].

Luca Zanni. An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines. Dipartimento di Matematica, Universitdi Modena e Reggio Emilia. [View Context].

Jeff G. Schneider and Andrew W. Moore. Active Learning in Discrete Input Spaces. School of Computer Science Carnegie Mellon University. [View Context].

Omid Madani and David M. Pennock and Gary William Flake. Co-Validation: Using Model Disagreement to Validate Classification Algorithms. Yahoo! Research Labs. [View Context].

Ron Kohavi and Barry G. Becker and Dan Sommerfield. Improving Simple Bayes. Data Mining and Visualization Group Silicon Graphics, Inc. [View Context].

**Citation Request:**

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## Evaluation

The evaluation of this dataset is done using Area Under the ROC curve (AUC).

An example of its application are ROC curves. Here, the true positive rates are plotted against false positive rates. An example is below. The closer AUC for a model comes to 1, the better it is. So models with higher AUCs are preferred over those with lower AUCs.

Please note, there are also other methods than ROC curves but they are also related to the true positive and false positive rates, e. g. precision-recall, F1-Score or Lorenz curves.

AUC is used most of the time to mean AUROC, AUC is ambiguous (could be any curve) while AUROC is not.

## Interpreting the AUROC

The AUROC has several equivalent interpretations:

- The expectation that a uniformly drawn random positive is ranked before a uniformly drawn random negative.
- The expected proportion of positives ranked before a uniformly drawn random negative.
- The expected true positive rate if the ranking is split just before a uniformly drawn random negative.
- The expected proportion of negatives ranked after a uniformly drawn random positive.
- The expected false positive rate if the ranking is split just after a uniformly drawn random positive.

## Computing the AUROC

Assume we have a probabilistic, binary classifier such as logistic regression.

Before presenting the ROC curve (= Receiver Operating Characteristic curve), the concept ofconfusion matrix must be understood. When we make a binary prediction, there can be 4 types of outcomes:

- We predict 0 while we should have the class is actually 0: this is called a True Negative, i.e. we correctly predict that the class is negative (0). For example, an antivirus did not detect a harmless file as a virus .
- We predict 0 while we should have the class is actually 1: this is called a False Negative, i.e. we incorrectly predict that the class is negative (0). For example, an antivirus failed to detect a virus.
- We predict 1 while we should have the class is actually 0: this is called a False Positive, i.e. we incorrectly predict that the class is positive (1). For example, an antivirus considered a harmless file to be a virus.
- We predict 1 while we should have the class is actually 1: this is called a True Positive, i.e. we correctly predict that the class is positive (1). For example, an antivirus rightfully detected a virus.

To get the confusion matrix, we go over all the predictions made by the model, and count how many times each of those 4 types of outcomes occur:

In this example of a confusion matrix, among the 50 data points that are classified, 45 are correctly classified and the 5 are misclassified.

Since to compare two different models it is often more convenient to have a single metric rather than several ones, we compute two metrics from the confusion matrix, which we will later combine into one:

- True positive rate (TPR), aka. sensitivity, hit rate, and recall, which is defined as . Intuitively this metric corresponds to the proportion of positive data points that are correctly considered as positive, with respect to all positive data points. In other words, the higher TPR, the fewer positive data points we will miss.
- False positive rate (FPR), aka. fall-out, which is defined as . Intuitively this metric corresponds to the proportion of negative data points that are mistakenly considered as positive, with respect to all negative data points. In other words, the higher FPR, the more negative data points we will missclassified.

To combine the FPR and the TPR into one single metric, we first compute the two former metrics with many different threshold (for example ) for the logistic regression, then plot them on a single graph, with the FPR values on the abscissa and the TPR values on the ordinate. The resulting curve is called ROC curve, and the metric we consider is the AUC of this curve, which we call AUROC.

The following figure shows the AUROC graphically:

In this figure, the blue area corresponds to the Area Under the curve of the Receiver Operating Characteristic (AUROC). The dashed line in the diagonal we present the ROC curve of a random predictor: it has an AUROC of 0.5. The random predictor is commonly used as a baseline to see whether the model is useful.

If you want to get some first-hand experience:

- Python: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
- MATLAB: http://www.mathworks.com/help/stats/perfcurve.html

*Source : http://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it*

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