CYBR7240 Assignment 3_whudso21
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William Hudson
CYBR7240
Assignment 3
(5 Points) When presented with a dataset, it is usually a good idea to visualise it first. Go to the
Visualise
tab. Click on any of the scatter plots to open a new window which shows the scatter plot for two selected attributes. Try visualising a scatter plot of
age
and
duration
. Do you notice anything unusual? You can click on any data point to display all it's values.
There is one outlier in the bottom left of the graph. Info of the outlier is shown on the screenshot
William Hudson
CYBR7240
Assignment 3
(5 Points) In the previous point you should have found a data point, which seems to be corrupted, as
some of its values are nonsensical. Even a single point like this can significantly affect the performance of a classifier. How do you think it would affect Decision trees? A good way to check this is to test the performance of each classifier before and after removing this datapoint.
It would skew the visualization to display more data in the right side of the graph due to several corrupted values being much lower than the “normal” dataset.
William Hudson
CYBR7240
Assignment 3
(10 Points) To remove this instance from the dataset we will use a filter. We want to remove all instances, where the age of an applicant is lower than 0 years, as this suggests that the instance is corrupted. In the
Preprocess
tab click on
Choose
in the Filter pane. Select
filters > unsupervised > instance > RemoveWithValues
. Click on the text of this filter to change the parameters. Set the attribute index to 13 (Age) and set the split point at 0. Click
Ok
to set the parameters and
Apply
to apply the filter to the data. Visualise the data again to verify that the invalid data point was removed.
William Hudson
CYBR7240
Assignment 3
(20 Points) On the
Classify
tab, select the
Percentage split
test option and change its value to 90%. This way, we will train the classifiers using 90% of the training data and evaluate their performance on the remaining 10%. First, train a decision tree classifier with default options. Select
classifiers > trees > J48
and click
Start
.
J48
is the Weka implementation of the
C4.5
algorithm, which uses the normalized information gain criterion to build a decision tree for classification.
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