Visualizing 1RThe purpose of the bo

Visualizing 1R
The purpose of the boundary visualizer is to show the predictions of a given model
for every possible combination of attribute values—that is, for every point in the
two-dimensional space. The points are color-coded according to the prediction the
model generates. We will use this to investigate the decision boundaries that different
classifiers generate for the reduced iris dataset.
Start with the 1R rule learner. Use the Choose button of the boundary visualizer
to select weka.classifiers.rules.OneR. Make sure you tick Plot training data; otherwise,
only the predictions will be plotted. Then click the Start button. The program
starts plotting predictions in successive scan lines. Click the Stop button once the
plot has stabilized—as soon as you like, in this case—and the training data will be
superimposed on the boundary visualization.
Exercise 17.3.1. Explain the plot based on what you know about 1R. (Hint:
Use the Explorer interface to look at the rule set that 1R generates for this
data.)
Exercise 17.3.2. Study the effect of the minBucketSize parameter on the
classifier by regenerating the plot with values of 1, and then 20, and then some
critical values in between. Describe what you see, and explain it. (Hint: You
could speed things up by using the Explorer interface to look at the rule sets.)
Now answer the following questions by thinking about the internal workings of
1R. (Hint: It will probably be fastest to use the Explorer interface to look at the
rule sets.)
Exercise 17.3.3. You saw earlier that when visualizing 1R the plot always has
three regions. But why aren’t there more for small bucket sizes (e.g., 1)? Use
what you know about 1R to explain this apparent anomaly.
Exercise 17.3.4. Can you set minBucketSize to a value that results in less than
three regions? What is the smallest possible number of regions? What is the
smallest value for minBucketSize that gives this number of regions? Explain
the result based on what you know about the iris data.

ผลลัพธ์ (ไทย) 1:

1r display screen of a scope. Display model predictions are defined for a set of attribute values ​​have - is that every point of the area of two-dimensional point is encoded according to the color of the - of the forecast models created us to take this decision in order to determine whether the scope. of different nouns iris create a data set for a lower start of the study with the rules. 1r use of the scope of the screen to select weka nouns. Rules oner sure you tick down the information, training and other predictions of a plot point it is only a button click. A. Start program began plotting predicted in the scan line of the Stop button, click the plot, which has stabilized as soon as you want. In this case, some of the information. The training will be superimposed image shows the extent we can catch her ​​exercise 17 and. 3.1 of explain, as you know, the plot is nothing we can catch her ​​on the 1r (hint: Explorer. Interactive user interface of the eyes of the law that created this 1r data) exercise 17 and. 3.2 Results of the study parameters. minbucketsize She has captured the classifier The rebuilding plan is the one already 20 of them, some of the critical during describe what you tlhap I explained to them (hint: you faster by using explorer interface to interface. a set of rules) now. Answer questions by thinking about the inner workings of 1r. (Hint: It may be applied to the Internet explorer interface as soon as I see the rule set) exercise 17 and. 3.3 you can see that. Earlier on display 1r plan has always been the three but no more for the smaller tank size (e. g 1) use what you know about 1r help explain the exercise, 17 and. 3.4 can minbucketsize. Set with less than three regions have a minimum number of regions is a minimal fee for. minbucketsize The number of regions to explain the results of the knowledge-based. You can catch her ​​about the iris.