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public:t-622-arti-14-1:lab_6_-_learning_decision_trees [2014/03/12 08:28]
stephan
public:t-622-arti-14-1:lab_6_-_learning_decision_trees [2014/03/12 08:42] (current)
stephan
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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). 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 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.
   - Compare this decision trees to the one that was learned by the algorithm using the whole training set and explain the difference.   - Compare this decision trees to the one that was learned by the algorithm using the whole training set and explain the difference.
  
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