You can find the Weka code in a file called "weka-src.jar", which should be located in the directory where Weka was installed. This "weka-src.jar" file is a zip file. Hence you need to unzip it to extract its contents. Inside, you will find the .java files that implement Weka.

Read the "Explorer Guide" and the "Experimenter Tutorial" provided with the Weka system. Browse through the "Package Documentation" to become familiar with it.

When needed, use the following command to increase the amount of main memory used by Weka. Here, I'm increasing the amount of main memory used by Weka to 768m, but you can specify any other size instead of 768 if more memory is needed/available:

java -Xmx768m -jar weka.jar

In particular,

• Use the data in the file "adult.data", and the description of the data in the file "adult.names".
• Use the nominal attribute "salary" (called >50K, ≤50K in the data files) as the classification target.

workclass,
education, and
sex

1. (5 points) Calculate the frequency and modes of the attribute.
2. (10 points) Provide a graphical depiction of the distribution of the target classification attribute for each of the values of the three aforementioned attribute under consideration.

age
education-num
capital-gain

1. (10 points) Calculate the percentiles (in increments of 10, as in Table 3.2 of the textbook, page 101), mean, median, range, and variance of the attribute.
2. (20 points) Plot a histogram of the attribute using 10 or 20 bins (you choose the best value for each attribute). For examples, see Figures 3.7 and 3.8 in the textbook, page 113.
3. (10 points) For the capital-gain attribute only, plot a graph in which the Y axis corresponds to the classification target (salary), the X axis is the capital-gain attribute, and for each data instance in the dataset, there is a point (x,y) in the plot where x and y are respectively the capital-gain value and the salary value of the data instance. For example, the plot will contain the point (2174, ≤50) which corresponds to the first data instance of the dataset.

1. (20 points) Calculate the covariance matrix and the correlation matrix of these attributes. See notes on using Matlab and Excel to calculate these matrices.
2. (5 points) If you had to remove some of these continuous attributes from the dataset based on these two matrices, which attributes would you remove if any and why? Explain your answer.

1. (5 points) Use Weka's unsupervised Resample filter to obtain a 50% subsample of the input dataset without replacement. Include in your report the distribution of the target attribute in the subsample.

2. (5 points) Use Weka's supervised Resample filter to obtain a 50% subsample of the input dataset without replacement. Include in your report the distribution of the target attribute in the subsample.

3. (5 points) Are the above two distributions different? Why is that?

1. (10 points) Use Weka's unsupervised Discretize filter to discretize the continuous attribute age of the input dataset into 10 bins using equal frequency (i.e., useEqualFrequency=True). Include the results in your report, as well as the distribution of the target attribute in each of the bins.

2. (10 points) Use Weka's unsupervised Discretize filter to discretize the continuous attribute age of the input dataset into 10 bins using equal width (i.e., useEqualFrequency=False). Include the results in your report, as well as the distribution of the target attribute in each of the bins.

3. (10 points) Use Weka's supervised Discretize filter to discretize the continuous attribute age of the input dataset with respect to the class attribute. Include the results in your report, as well as the distribution of the target attribute in each of the resulting bins.

4. Weka Code. Find the Java code that implements the unsupervised discretization filter in the directories that contain the Weka files, following the instructions provided above.
   1. (5 points) Include the first 20 lines of the code implementing this filter in your report, and
   2. (10 points) Describe the algorithm followed by this code when doing unsupervised equal-frequency discretization (i.e., useEqualFrequency=True) in your own words.

Use Weka's unsupervised ReplaceMissingValues filter to fill in the missing values in the attribute occupation.

1. (10 points) Describe the Weka code implementing this filter in your report.
2. (10 points) Compare the distribution of the original occupation attribute against the distribution of this attribute after replacing the missing values.

Apply Principal Components Analysis to reduce the dimensionality of the input dataset. For this, use Weka's PrincipalComponents option from the "Select attributes" tab. Use parameter values: centerData=True, varianceCovered=0.95.

1. (3 points) How many dimensions (= attributes) does the original dataset contain?
2. (3 points) How many dimensions are obtained after PCA?
3. (3 points) How much of the variance do they explain?
4. (5 points) Include in your report the linear combinations that define the first two new attributes(= components) obtained.
5. (6 points) Look at the results and elaborate on any interesting observations you can make about the results.

Apply Correlation Based Feature Selection (see Witten's and Frank's textbook slides - Chapter 7 Slides 5-6) to the input dataset. For this, use Weka's CfsSubsetEval available under the Select attributes tab with default parameters.

1. (3 points) Include in your report which attributes were selected by this method.
2. (5 points) Also, what can you observe about these selected attributes with respect to the covariance matrix and the correlation matrix you computed for part 2.4.2 above?
3. (2 points) Were the 3 attributes you chose to remove in part 2.4.2 above kept or removed by CfsSubsetEval?