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Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees.
An ensemble of models employing the random subspace method can be constructed using the following algorithm: Let the number of training points be N and the number of features in the training data be D. Let L be the number of individual models in the ensemble. For each individual model l, choose n l (n l < N) to be the number of input points for l.
E-mail spam problem is a common classification problem, in this problem, 57 features are used to classify spam e-mail and non-spam e-mail. Applying IJ-U variance formula to evaluate the accuracy of models with m=15,19 and 57.
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis.
The simplest is to add k binary features to each sample, where each feature j has value one iff the jth centroid learned by k-means is the closest to the sample under consideration. [6] It is also possible to use the distances to the clusters as features, perhaps after transforming them through a radial basis function (a technique that has been ...
Data filtering: Use either R code or a drag-and-drop GUI to select cases of interest. Full data editing with one-click recoding; full undo / redo functionality, Compute columns via R code (e.g. via row-wise functions like rowMean, rowMeanNaRm, rowSum, rowSD ...) or a drag-and-drop GUI to create new variables or compute them from existing ones.
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Discussions of some more exotic generalizations of random forests. There are a lot of neat, somewhat exotic models which use random forests as a base, but this has the same risk as a list of links. Significantly more examples, similar to sections 3.3,4.3,5.3,6.3,etc of the Criminisi paper I linked above.