Search results
Results from the WOW.Com Content Network
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.
Rattle provides considerable data mining functionality by exposing the power of the R Statistical Software through a graphical user interface. Rattle is also used as a teaching facility to learn the R software Language. There is a Log Code tab, which replicates the R code for any activity undertaken in the GUI, which can be copied and pasted.
When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each sample is only considered out-of-bag for the trees that do not include it in their bootstrap sample.
Filter feature selection is a specific case of a more general paradigm called structure learning.Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph.
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.
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
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.