public:t-622-arti-13-1:lab_6_-_learning_decision_trees
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| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| public:t-622-arti-13-1:lab_6_-_learning_decision_trees [2013/03/19 13:18] – stephan | public:t-622-arti-13-1:lab_6_-_learning_decision_trees [2024/04/29 13:33] (current) – external edit 127.0.0.1 | ||
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| Tasks: | Tasks: | ||
| - Change the existing code, so that it prints the necessary data for a learning curve. That is, change the code such that it learns trees for increasing numbers of training examples and test each of the trees using the test set. | - Change the existing code, so that it prints the necessary data for a learning curve. That is, change the code such that it learns trees for increasing numbers of training examples and test each of the trees using the test set. | ||
| - | - Plot the learning curves for all three data sets (e.g., using MS Excel, LibreOffice, | + | - Plot the learning curves for all three data sets (e.g., using a spreadsheet application of your choice) (monk-1, monk-2, monk-3) and interpret them. |
| - Are all the trees that are learned consistent with the training data (consistent = all examples are classified correctly)? If not, what could be the reason? (Hint: Consistency of the tree with the training set can be checked easily by adding a single line of code.) | - Are all the trees that are learned consistent with the training data (consistent = all examples are classified correctly)? If not, what could be the reason? (Hint: Consistency of the tree with the training set can be checked easily by adding a single line of code.) | ||
| - Look at the true functions for the three data sets in monk.names (Section 9). Design a good decision tree for the concept of monks-1 by hand. | - Look at the true functions for the three data sets in monk.names (Section 9). Design a good decision tree for the concept of monks-1 by hand. | ||
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