J48
What you should know before reading further:
- For who: Data scientist
- Which phase: Deployment
- Ethical principles: Explicability; Transparency
- Type of Machine Learning: Supervised
- Type of Explainability: Explain by interpretation
- Skill level: Medium
- Status of model: Evaluated
- Estimated Time Effort:???
ID3
ID3 (by Ross Quinlan) is a simple, intuitive method to generate a decision trees from a given data-set.
The algorithm automatically selects attributes with higher
discriminative power to be put higher up into the tree, such that
smaller trees are learnt. (based on for instance information gain)
As
the method will continue to add branching nodes to the tree until all
instances in the dataset are correctly classified (if possible), the
method tends to overfit.
- The method is simple to use.
- It is not guaranteed that the smallest decision tree is learnt:
- As the method is a greedy method, it can be stuck in local optima.
- ID3 only works on categorical data.
- As the generated trees are available to the users and clear to interpret, such method provides high level of explainability.
- In order to reduce overfitting, trees can be pruned into smaller trees, which tend to better generalize.
C45
C45 (by Ross Quinlan)
is a method to generate a decision tree from a given dataset. It is an
extension of the earlier proposed ID3 method. Compared to ID3:
- it also works on continuous data;
- it provides pruning of the generated tree to reduce the risk of overfitting;
- it can work with missing values.
Just like ID3, this method is easy to use and the generated decision trees are available to the users, can be interpreted
and it can be motivated why the algorithm ordered the nodes in the tree in the given way. Hence, this method provides a high level of explainability.
Sometimes C45 is referred to as J48, which is an open source implementation of the method.
C45
C45
is a method to generate a decision tree from a given dataset. It is an
extension of the earlier proposed ID3 method. Compared to ID3:
- it also works on continuous data;
- it provides pruning of the generated tree to reduce the risk of overfitting;
- it can work with missing values.
Just like ID3, this method is easy to use and the generated decision trees are available to the users, can be interpreted
and
it can be motivated why the algorithm ordered the nodes in the tree in
the given way. Hence, this method provides a high level of
explainability.
For more information about J48 go here.
Mentioned in:
This conceptual method was created by Alonso et al. (2019) to create natural explanations to children using single classifiers.
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