public:t-622-arti-16-1:lab_6_-_learning_decision_trees
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| - Plot the learning curves for all three data sets (e.g., using a spreadsheet application of your choice) and interpret them. | - Plot the learning curves for all three data sets (e.g., using a spreadsheet application of your choice) 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 of the file). These are the functions that were used to generate the training and test data. Now that you know the function, design a good decision tree for monks-1 by hand. | + | - Look at the true functions for the three data sets in monk.names (Section 9 of the file). These are the functions that were used to generate the training and test data. Now that you know the function, design a good (small) |
| - Compare this decision tree to the one that was learned by the algorithm using the whole training set and explain the difference. | - Compare this decision tree to the one that was learned by the algorithm using the whole training set and explain the difference. | ||
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