Contents
What is a one leaf decision tree?
A special case of a decision tree is a decision list, which is a one-sided decision tree, so that every internal node has exactly 1 leaf node and exactly 1 internal node as a child (except for the bottommost node, whose only child is a single leaf node).
What happens if we do not limit the size of decision tree?
If there is no limit set on a decision tree, it will give you 100% accuracy on the training data set because in the worse case it will end up making 1 leaf for each observation. Thus this affects the accuracy when predicting samples that are not part of the training set.
What is minimum leaf size?
Leaf is the end node of a decision tree. A smaller leaf makes the model more prone to capturing noise in train data. Generally, I prefer a minimum leaf size of more than 50. However, you should try multiple leaf sizes to find the most optimum for your use case.”
How do you reduce the size of a decision tree?
Pruning reduces the size of decision trees by removing parts of the tree that do not provide power to classify instances. Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this likelihood.
What is minimal gain?
From my understanding, the minimal gain is where we are setting a cut point (threshold). For example, if the calculated gain for a particular feature split is greater than the set minimal gain then there will be a split else it will just leave that feature as a leaf.
Can you solve a case without a decision tree?
An example of a decision tree is shown below. Finally when you complete the decision tree, the branches must be prioritized and hypotheses are developed ONLY for the prioritized branches. You can sometimes solve a case without hypotheses because the drivers are so specific and point out the problem.
Why are my decision tree leafs not pure?
With default settings the DecisionTreeClassifier does not have any restrictions in terms of complexity as described in the Scikit Documentation. Therefore, it will stop further branching the tree if either a given node is pure (all examples have the same classification) or there are no further attributes to branch on.
Why is a decision tree called a tree?
When formed together, these elements loosely resemble a tree, which is where the diagram gets its name. Although you can certainly make a case for Grandmother Willow’s age old advice to “let your spirits guide you”, sometimes, a more formalized and calculated approach is necessary. This is why decision trees are so effective.
How are the drivers of a decision tree exhaustive?
Each level of drivers/branches of the decision tree must be mutually exclusive and collectively exhaustive (MECE). All the drivers/branches are collectively called the framework/structure for the case. An example of a decision tree is shown below.