artificial neural networks, genetic algorithms, density estimation, clustering, abstraction, discretisation, visualisation, detecting changes in data or models
Small variations can change chosen splits at high level nodes, which then changes subtree below
Conclusions about attribute importance can be unreliable
Direct methods tend to overfit training dataset
This problem can be reduced by pruning the tree
Another approach that often works well is to fit the tree, remove all training cases that are not correctly predicted, and refit the tree on the reduced dataset
Typically gives a smaller tree
This usually works almost as well on the training data
But generalises better, e.g. works better on test data
Bagging the tree algorithm also gives more stable results
Information criteria such as AIC and BIC are often used to decide how many are appropriate
Extending to multiple attributes is easy if we assume they are independent, at least conditioning on segment membership
It is possible to introduce associations, but this can rapidly increase the number of parameters required
Nominal attributes can be accommodated by allowing different discrete distributions in each latent class, and assuming conditional independence between attributes
Can extend this approach to a handle joint clustering and prediction models, as mentioned in the MVA lectures
Be the first to comment