Summary

In this chapter, we looked at the following topics:

  • Ensemble methods
  • Bagging
  • AdaBoost
  • Random forests
  • BBN, which provides a topology model of causal relationship; it is an eager learner.

    Tip

    An eager learner is one that behaves in an opposite way to the lazy learner. The lazy learner postpones the learning until the test tuple or test instance is provided.

  • The kNN algorithm, which is a lazy learner
  • SVM, by which the original data is transformed to a higher dimension, and is separated with a hyper-plane; it is an eager learner
  • Classification using frequent patterns; this is an eager learner
  • Classification using the BP algorithm, which is a neural network trained with a descent gradient

In the next chapter, we will learn the clustering algorithm, which is also a kind of unsupervised classification with no predefined labels.

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