In multi-label classification, this is the subset accuracy which is a harsh.
The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting.
Cost complexity pruning provides another option to control the size of a tree. In DecisionTreeClassifier, this pruning technique is parameterized by the Missing: Fenton MI.
min_samples_leaf int or float, default=1.
Below is the logic of the decision tree of the final model.
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of Missing: Fenton MI. Sep 13, Class for doing pruning of a sci-kit learn DecisionTreeClassifier. At initialization, the order of the nodes to prune is found, but no pruning is done.
The complexity is some measure of how complicated the three is; in our case, the complexity is the number of nodes in the tree.
The order of pruning is determined by the pruning that results in the smallest increase in the cost (e.g. entropy or gini index)Missing: Fenton MI.
If you really want to use sgenoud's 7-year-old fork of scikit-learn from back ingit clone on the base directory of the repo, don't just try to copy/clone individual files (of course you'll be losing any improvements/fixes since; way back on v ). But that idea sounds misconceived: you can get shallower/pruned trees by changing parameters to get early stopping Missing: Fenton MI. Decision Tree with PEP,MEP,EBP,CVP,REP,CCP,ECP pruning,all are implemented with Python(sklearn-decision-tree-prune included,All finished).
- ChengDale/Decision_Tree_PruneMissing: Fenton MI.