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What is complexity parameter in decision tree?
The complexity parameter (cp) is used to control the size of the decision tree and to select the optimal tree size. If the cost of adding another variable to the decision tree from the current node is above the value of cp, then tree building does not continue.
Can decision trees capture complex patterns in the data?
The shallower the tree the less variance we have in our predictions; however, at some point we can start to inject too much bias as shallow trees (e.g., stumps) are not able to capture interactions and complex patterns in our data. This results in high variance and poor generalizability.
What is min split in decision tree?
Min Split. The minimum number of values in a node that must exist before a split is attempted. In other words, if the node has two members and the minimum split is set to 5, the node will become terminal, that is, no split will be attempted.
How to reduce the complexity of a decision tree?
In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Greater values of ccp_alpha increase the number of nodes pruned.
How is complexity parameter used in data mining?
The complexity parameter (cp) is used to control the size of the decision tree and to select the optimal tree size.
How to prune a tree with minimal cost complexity?
See also Minimal Cost-Complexity Pruning for details on pruning. Minimal cost complexity pruning recursively finds the node with the “weakest link”. The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first.
How to control the size of a tree?
Cost complexity pruning provides another option to control the size of a tree. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha.