# Classification and association rules for census income data

### Introduction

In this blog post I am going to show (some) analysis of census income data — the so called “Adult” data set, [1] — using three types of algorithms: decision tree classification, naive Bayesian classification, and association rules learning. Mathematica packages for all three algorithms can be found at the project MathematicaForPrediction hosted at GitHub, [2,3,4].

(The census income data set is also used in the description of the R package “arules”, [7].)

In the census data every record represents a person with 14 attributes, the last element of a record is one of the labels {“>=50K”,”<50K”}. The relationships between the categorical variables in that data set was described in my previous blog post, “Mosaic plots for data visualization”.

For this data the questions I am most interested in are:
Question 1: Which of the variables (age, occupation, sex, etc.) are most decisive for determining the income of a person?
Question 2: Which values for which variables form conditions that would imply high income or low income? (I.e. “>50K” or “<=50K”.)
Question 3: What conclusions or confirmations we can get from answering the previous two questions?

One way to answer Question 1 is to use following steps, [8].
1. Build a classifier with the training set.
2. Verify using the test set that good classification results are obtained.
3. If the number of variables (attributes) is k for each i, 1<=i<=k :
3.1. Shuffle the values of the i-th column of the test data and find the classification success rates.
4. Compare the obtained k classification success rates between each other and with the success rates obtained by the un-shuffled test data.
5. The variables for which the classification success rates are the worst are the most decisive.

Following these steps with a decision tree classifier, [2], I found that “marital-status” and “education-num” (years of education) are most decisive to give good prediction for the “>50K” label. Using a naive Bayesian classifier, [3], the most significant variables are “marital-status” and “relationship”. (More details are given in the sections “Application of decision trees” and “Application of naive Bayesian classifier”.)

One way to answer Question 2 is to find which values of the variables (e.g. “Wife”, “Peru”, “HS-grad”, “Exec-managerial”) associate most frequently with “>50K” and “<=50K” respectively and apply different Bayesian probability statistics on them. This is what the application of Associative rules learning gives, [9]. Another way is to use mosaic plots, [5,9], and prefix trees (also known as “tries”) [6,11,12].

In order to apply Association rule learning we need to make the numerical variables categorical — we need to partition them into non-overlapping intervals. (This derived, “all categorical” data is also amenable to be input data for mosaic plots and prefix trees.)

Insights about the data set using Mosaic Plots can be foundĀ in my previous blog post “Mosaic plots for data visualization”, [13]. The use of Mosaic Plots in [13] is very similar to the Naive Bayesian Classifiers application discussed below.

### Data set

The data set can be found and taken from http://archive.ics.uci.edu/ml/datasets/Census+Income, [1].

The description of the data set is given in the file “adult.names” of the data folder. The data folder provides two sets with the same type of data “adult.data” and “adult.test”; the former is used for training, the latter for testing.

The total number of records in the file “adult.data” is 32561; the total number of records in the file “adult.test” is 16281.

Here is how the data looks like:

Since I did not understand the meaning of the column “fnlwgt” I dropped it from the data.

Here is a summary of the data:

As it was mentioned in the introduction, only 24% of the labels are “>50K”. Also note that 2/3 of the records are for males.

### Scatter plots and mosaic plots

Often scatter plots and mosaic plots can give a good idea of the general patterns that hold in the data. This sub-section has a couple of examples, but presenting extensive plots is beyond the scope of this blog post. Let me point out that it might be very beneficial to use these kind of plots with Mathematica‘s dynamic features (like Manipulate and Tooltip), or make a grid of mosaic plots.

Mosaic plots of the categorical variables of the data can be seen in my previous blog post “Mosaic plots for data visualization”.

Here is a table of the histograms for “age”, “education-num”, and “hours-per-week”:

Here is a table with scatter plots for all numerical variables of the data:

### Application of decision trees

The building and classification with decision trees is straightforward. Since the label “>50K” is only a quarter of the records I consider the classification success rates for “>50K” to be more important.

I experimented with several sets of parameters for decision tree building. I did not get a classification success rate for “>50K” better than 0.644 . Using pruning based on the Minimal Description Length (MDL) principle did not give better results. (I have to say I find MDL pruning to be an elegant idea, but I am not convinced that it works that
well. I believe decision tree pruning based on test data would produce much better results. Only the MDL decision tree pruning is implemented in [2].)

The overall classification success rate is in line with the classification success ratios listed in explanation of the data set; see the file “adult.names” in [1].

Here is a table with the results of the column shuffling experiments described in the introduction (in red is the name of the data column shuffled):

Here is a plot of the “>50K” success rates from the table above:

We can see from the table and the plot that variables “marital-status”, “education-num”, “capital-gain”, “age”, and “occupation” are very decisive when it comes to determining high income. The variable “marital-status” is significantly more decisive than the others.

While considering the decisiveness of the variable “marital-status” we can bring the following questions:
1. Do people find higher paying jobs after they get married?
2. Are people with high paying jobs more likely to marry and stay married?

Both questions are probably answered with “Yes” and probably that is why “marital-status” is so decisive. It is hard to give quantified answers to these questions just using decision trees on this data — we would need to know the salary and marital status history of the individuals (different data) or to be able to imply it (different algorithm).

We can see the decisiveness of “age”, “education-num”, “occupation”, and “hours-per-week” as natural. Of course one is expected to receive a higher pay if he has studied longer, has a high paying occupation, is older (more experienced), and works more hours per week. Note that this statement explicitly states the direction of the correlation: we do assume that longer years of study bring higher pay. It is certainly a good idea to consider the alternative direction of the correlation, that people first get high paying jobs and that these high paying jobs allow them to get older and study longer.

### Application of naive Bayesian classifiers

The naive Bayesian classifier, [3], produced better classification results than the decision trees for the label “>50K”:

Here is a table with the results of the column shuffling experiments described in the introduction (in red is the name of the data column shuffled):

Here is a plot of the “>50K” success rates from the table above:

In comparison with the decision tree importance of variables experiments we can notice that:
1. “marital-status” is very decisive and it is the second most decisive variable;
2. the most decisive variable is “relationship” but it correlates with “marital-status”;
3. “age”, “occupation”, “hours-per-week”, “capital-gain”, and “sex” are decisive.

### Shuffled classification rates plots comparison

Here are the two shuffled classification rates plots stacked together for easier comparison:

### Data modification

In order to apply the association rules finding algorithm Apriori, [4], the data set have to be modified. The modification is to change the numerical variables “age”, “education-num”, and “age” into categorical. I just partitioned them into non-overlapping intervals, labeled the intervals, and assigned the labels according the variable values. Here is the summary of the modified data for just these variables:

### Finding association rules

Using the modified data I found a large number of association rules with the Apriori algorithm, [4]. I used the measure called “confidence” to extract the most significant rules. The confidence of an association rule AāC with antecedent A and consequent C is defined to be the ratio P(A ā© C)/P(C). The higher the ratio the more confidence we have in the rule. (If the ratio is 1 we have a logical rule, C ā A.)

Here is a table showing the rules with highest confidence for the consequent being “>50K”:

From the table we can see for example that 2.1% of the data records (or 693 records) show that for a married man who has studied 14 years and originally from USA there is a 0.79 probability that he earns more than \$50000.

Here is a table showing the rules with highest confidence for the consequent being “<=50K”:

The association rules in these tables confirm the findings with the classifiers: marital status, age, and education are good predictors of income labels “>50K” and “<=50K”.

### Conclusion

The analysis confirmed (and quantified) what is considered common sense:

Age, education, occupation, and marital status (or relationship kind) are good for predicting income (above a certain threshold).

Using the association rules we see for example that
(1) if a person earns more than \$50000 he is very likely to be a married man with large number of years of education;
(2) single parents, younger than 25 years, who studied less than 10 years, and were never-married make less than \$50000.

### References

[1] Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Census Income Data Set, URL: http://archive.ics.uci.edu/ml/datasets/Census+Income .

[2] Antonov, A., Decision tree and random forest implementations in Mathematica, source code at https://github.com/antononcube/MathematicaForPrediction, package AVCDecisionTreeForest.m, (2013).

[3] Antonov, A., Implementation of naive Bayesian classifier generation in Mathematica, source code at GitHub, https://github.com/antononcube/MathematicaForPrediction, package NaiveBayesianClassifier.m, (2013).

[4] Antonov, A., Implementation of the Apriori algorithm in Mathematica, source code at https://github.com/antononcube/MathematicaForPrediction, package AprioriAlgorithm.m, (2013).

[5] Antonov, A., Mosaic plot for data visualization implementation in Mathematica, source code at GitHub, https://github.com/antononcube/MathematicaForPrediction, package MosaicPlot.m, (2014).

[6] Antonov, A., Tries with frequencies Mathematica package, source code at GitHub, https://github.com/antononcube/MathematicaForPrediction, package TriesWithFrequencies.m, (2013).

[7] Hahsler, M. et al., Introduction to arules [Dash] A computational environment for mining association rules and frequent item sets, (2012).

[8] Breiman, L. et al., Classification and regression trees, Chapman & Hall, 1984.

[9] Wikipedia, Association rules learning, http://en.wikipedia.org/wiki/Association_rule_learning .

[10] Antonov, A., Mosaic plots for data visualization, (March, 2014), MathematicaForPrediction at GitHub, URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/Documentation/Mosaic%20plots%20for%20data%20visualization.pdf .

[11] Wikipedia, Trie, http://en.wikipedia.org/wiki/Trie .

[12] Antonov, A., Tries, (December, 2013), URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/Documentation/Tries.pdf .

[13] Antonov, A., Mosaic plots for data visualization, (March, 2014) MathematicaForPrediction at WordPress.

# Enhancements of MosaicPlot

I made the following enhancements of the function `MosaicPlot` which I described (and proclaimed the implementation of) in my previous blog post:

1. Tooltips with precise contingency statistics.
2. If the last data column is numerical then `MosaicPlot` can use it as pre-computed contingency statistics.
3. Coloring of the rectangles according to a list of index->color rules.

The document “Mosaic plots for data visualization” hosted at MathematicaForPrediction at GitHub, combines the information of this blog post and previous one. The document also has Mathematica code examples of usage and description of `MosaicPlot`‘s options.

### Tooltips with precise contingency statistics

I already proclaimed in my previous blog post the tooltips functionality — when hovering with the mouse over the rectangles then `MosaicPlot`, using `Tooltip`, gives a table with the exact co-occurrence (contingency) values. Here is an example:

### Visualizing categorical columns + a numerical column

If the last data column is numerical then `MosaicPlot` can use it as pre-computed contingency statistics. This functionality is specified with the option “ExpandLastColumn”->True.

In order to explain the functionality we are going to use following interpretation. If the last of column of the data is numerical then we can treat the data as a contracted version of a longer list of records made only of the categorical columns. For example, consider the following table with observations of people’s hair and eyes color:

The table above can be considered as a contracted version of this table:

Setting the option “ExpandLastColumn” to True gives a mosaic plot corresponding to that latter, observations-expanded table:

The last data column (which is numerical) does not need to be made of integers:

### Rectangle coloring

The rectangles can be colored using the option ColorRules which specifies how the colors of the rectangles are determined from the indices of the data columns.

More precisely, the values of the option ColorRules should be a list of rules, {i1->c1,i2->c2,…}, matching the form

`{(_Integer->(_RGBColor|_GrayLevel))..} .`

If coloring for only one column index is specified the value of ColorRules can be of the form

`{_Integer->{(_RGBColor|_GrayLevel)..}} .`

The colors are used with `Blend` in order to color the rectangles according to the order of the unique values of the specified data columns.

The default value for `ColorRules` is `Automatic`. When `Automatic` is given to `ColorRules`, `MosaicPlot` finds the data column with the largest number of unique values and colors them according to their order using `ColorData[7,"ColorList"]`.

The grid of plots below shows mosaic plots of the same data with different values for the option `ColorRules` (given as plot labels).

# Mosaic plots for data visualization

### Introduction

This blog post has description and examples of using the function `MosaicPlot` of the Mathematica package MosaicPlot.m provided by the project MathematicaForPrediction at GitHub. (Also see the document “Mosaic plots for data visualization” hosted at MathematicaForPrediction at GitHub. The document also has Mathematica code examples of usage and description of `MosaicPlot`‘s options.)

The function `MosaicPlot` summarizes the conditional probabilities of co-occurrence of the categorical values in a list of records of the same length. The list of records is assumed to be a full array and the columns to represent categorical values. (Note, that if a column is numerical but has a small number of different values it can be seen as categorical.)

I have read the descriptions of mosaic plots in the book “R in Action” by Robert Kabakoff and one of the references provided in the book (“What is a mosaic plot?” by Steve Simon). I was impressed how informative mosaic plots are and I figured they can be relatively easily implemented using Prefix trees (also known as “Tries”). I implemented MosaicPlot while working on a document analyzing the census income data from 1998, [6]. This is the reason that data set is used in this blog post. A good alternative set provided by ExampleData is {“Statistics”,”USCars1993″}.

### Data set

The data set can be found and taken from http://archive.ics.uci.edu/ml/datasets/Census+Income.

The description of the data set is given in the file “adult.names” of the data folder. The data folder provides two sets with the same type of data “adult.data” and “adult.test”; the former is used for training, the latter for testing.

The total number of records in the file “adult.data” is 32561; the total number of records in the file “adult.test” is 16281.

Here is how the data looks like:

Since I did not understand the meaning of the column “fnlwgt” I dropped it from the data.

Here is the summary table of the data:

On the summary table the numerical variables are described with min, max, and quartiles. The category variables are described with the tallies of their values. The tallies of values are ordered in decreasing order. The tallies of truncated values are summed under the value “(Other)”.

Note that:
— only 24% of the labels are “>50K”;
— 2/3 of the records are for males;
— “capital-gain” and “capital-loss” are very skewed.

### Mosaic plot explanations

If we pick a categorical variable, say “sex”, we can visualize the frequencies of the appearance of the variable values with the following plot:

The size of the rectangles depends on the frequencies of appearance of the values “Male” and “Female” in the data records. From the rectangle sizes we can see what we already knew from the data summary table: approximately 2/3 of the records are about males.

We can subdivide every rectangle r according to the frequencies of co-occurrence of r’s value with the values of a second categorical variable, say “relationship”:

The labels corresponding to the values of “relationship” are rotated for legibility. The “relationship” labels are placed according to the co-occurrence with the value “Male” of the variable “sex”. The correspondent fractions of the pairs (“Female”,”Husband”), (“Female”,”Not-in-family”), etc., are deduced from order of the “relationship” labels.

Using colored mosaic plots can help distinguishing which rectangles correspond to which values. Here is the last plot with rectangles colored across the “relationship” data variable:

From the visual representations of the “sex vs. relationship” mosaic plot we can see that large fraction of the males are husbands, none (or a very small fraction) of them are wives. We can also see that none (or a very small fraction) of the females are husbands, the largest fraction of them are “Not-in-family”, and they are approximately three times more than the females that are wives.

Let us make another mosaic plot of a different kind of relationship “sex vs. education”:

By comparing the sizes of the rectangles corresponding to the values “Bachelors”, “Doctorate”, “Masters”, and “Some-college” on the “sex vs. education” mosaic plot we can see that the fraction of men that have finished college is larger than the fraction of women that have finished college.

We can further subdivide the rectangles according to the co-occurrence frequencies with a third categorical variable. We are going to choose that third variable to be “income”, the values of which can be seen as outcomes or consequents of the values of the first two variables of the mosaic plot.

From the mosaic plot “sex vs. education vs. income” we can make the following observations.
1. Approximately 75% of the males with doctorate degrees or with a professional school degree earn more than \$50000 per year.
2. Approximately 60% of the females with a doctorate degree earn more than \$50000 per year.
3. Approximately 45% of the females with a professional school degree earn more than \$50000.
4. Across all education type females are (much) less likely to earn more than \$50000 per year.

Although I mentioned earlier that the “outcome” variable should be the last variable in the mosaic plot, it is also useful to start with the outcome variable to get an attribute breakdown perspective (using a different color scheme):

### Signature of MosaicPlot

`MosaicPlot` takes various options for tweaking the labels placement and style. Here is the Mathematica command:
``` MosaicPlot[censusData[[All, {9, 3, 5, 14}]], "Gap" -> 0.014, "ColumnNamesOffset" -> 0.07, "ColumnNames" -> Map[Style[#, Blue, FontSize -> 15] &, columnNames[[{9, 3, 5, 14}]]], "LabelRotation" -> {{3, 1}, {1, 1}}, ImageSize -> 900] ```
with which the following mosaic plot was made:

The option “Gap” used to regulate the gaps between the rectangle. The options “ColumnNames” and “ColumnNamesOffset” are for the specification of the variable names (in blue in the plot). The option “LabelRotation” specifies the rotation of the labels that correspond to the individual values of the variables. Also, `MosaicPlot` takes all the options of `Graphics` (since it is based on it).

### Tooltip tables

The function `MosaicPlot` has an interactive feature using `Tooltip` that gives a table with the exact co-occurrence (contingency) values when hovering with the mouse over the rectangles. Here is an example:

### Future plans

The current implementation of `MosaicPlot` uses coloring of the rectangles for easier plot reading. An alternative is to use coloring based on correlations statistics. I think though that the tooltip contingency tables with flexible coloring specification make the correlation coloring less needed.