What are pure leaves in decision tree?
If this is the case, the tree is done. If not, the leaf nodes are again split until eventually all leaves are pure (i.e. all its data points contain the same label) or cannot be split any further (in the rare case with two identical points of different labels).
Where is the best Hyperparameter for a decision tree?
The best way to tune this is to plot the decision tree and look into the gini index. Interpreting a decision tree should be fairly easy if you have the domain knowledge on the dataset you are working with because a leaf node will have 0 gini index because it is pure, meaning all the samples belong to one class.
When to prune a decision tree in Computer Science?
An alternate approach is to prune the tree to maximize classification performance on a validation set (a data set with known labels, which was not used to train the tree). We pass the validation data down the tree. At each node, we record the total number of instances and the number of misclassifications, if that node were actually a leaf.
How to split nodes in a decision tree?
Identify feature that results in the greatest information gain ratio. Set this feature to be the splitting criterion at the current node. If the best information gain ratio is 0, tag the current node as a leaf and return. Partition all instances according to attribute value of the best feature.
How is a decision tree learning algorithm learned?
The decision tree learning algorithm recursively learns the tree as follows: Assign all training instances to the root of the tree. Set curent node to root node. Partition all data instances at the node by the value of the attribute. Compute the information gain ratio from the partitioning.
What is the purpose of a decision tree?
Caption: Decision tree to determine type of contact lens to be worn by a person. The known attributes of the person are tear production rate, whether he/she has astigmatism, their age (categorized into two values) and their spectacle prescription. Decision trees can be learned from training data.