Posts Tagged ‘Classifiers’

Support Vector Machine

SVMs are classifiers that construct a maximum margin hyperplane between positive and negative examples, which is then used to classify unseen examples.

Random Forests

Random Forests are collections of decision trees, in which each individual decision tree is learned in a standard way but with the exception that only a small random subset of the attributes is available for learning. the votes of the individual trees in the forest are then averaged to classify new examples.

Boosted Decision Tree

Boosted decision trees refer to a method in which each decision tree is learned on the entire dataset, but after the first tree is learned, the weights of the instances in the data are adjusted so that incorrectly classified example are given a higher weight. the decision tree algorithm is then run again, but this time with a focus on the “harder” examples. this process is repeated many times. the resulting collection of decision trees classifies new examples in a similar manner to the Random Forests, by averaging the individual predictions, except that the individual trees are weighted.