Due: F 5/7 @ 11:59 P.M.
Last one! \o/
import java.lang.reflect.*; ... case "-b": // base learner Class<?> c = Class.forName( options[++a] ); this.classifier = (Classifier) c.getDeclaredConstructor().newInstance();As an example, to bag decision stumps, we can use the command:
$ java Bagging -b WDS -t vote.mff
The implementations must follow sound principles of object-oriented Implement each learner as a single executable. No windows. No menus. No prompts. Just do it.
The logic of the implementation should be the same as that for the previous implementations. If the user runs a learner and specifies only a training set, then the program should evaluate using 10-fold cross-validation and output the results. Naturally, the user can use the -x switch to change the default. If the user provides the -p switch and a proportion, then the program conducts an evaluation using the hold-out method. Otherwise, if the user specifies both a training and testing set, then the program should build a model from the training set, evaluate it on the testing set, and output the results.
In accordance with the class policies and Georgetown's Honor System, I certify that, with the exceptions of the class resources and those items noted below, I have neither given nor received any assistance on this project. Name NetIDInclude this file in your zip file submit.zip.
Submit p5 exactly like you submitted p4. Make sure you remove all debugging output before submitting.
If Autolab is down, upload your zip file to Canvas.
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