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Note: Most tree growing methods favor greatest impurity reduction near the root node. | Note: Most tree growing methods favor greatest impurity reduction near the root node. | ||
| + | Ex. | ||
| + | [[Image:Lecture22_DecisionTree_OldKiwi.JPG]] | ||
| + | |||
| + | To assign category to a leaf node. | ||
| + | Easy! | ||
| + | If sample data is pure | ||
| + | => assign this class to leaf. | ||
| + | else | ||
| + | => assign the most frequent class. | ||
| + | |||
| + | Note: Problem of building decision tree is "ill-conditioned" | ||
| + | i.e. small variance in the training data can yield large variations in decision rules obtained. | ||
| + | |||
| + | Ex. p.405(D&H) | ||
| + | A small move of one sample data can change the decision rules a lot. | ||
| + | |||
| + | Reference about clustering | ||
| + | "Data clustering, a review" A. K. Jain, M. N. | ||
Revision as of 10:42, 3 April 2008
Note: Most tree growing methods favor greatest impurity reduction near the root node.
Ex.
To assign category to a leaf node. Easy! If sample data is pure
=> assign this class to leaf.
else
=> assign the most frequent class.
Note: Problem of building decision tree is "ill-conditioned" i.e. small variance in the training data can yield large variations in decision rules obtained.
Ex. p.405(D&H) A small move of one sample data can change the decision rules a lot.
Reference about clustering "Data clustering, a review" A. K. Jain, M. N.
