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Illustration of training a Random Forest model. The training dataset (in this case, of 250 rows and 100 columns) is randomly sampled with replacement n times. Then, a decision tree is trained on each sample. Finally, for prediction, the results of all n trees are aggregated to produce a final decision.
The random forest classifier operates with a high accuracy and speed. [11] Random forests are much faster than decision trees because of using a smaller dataset. To recreate specific results, it is necessary to keep track of the exact random seed used to generate the bootstrap sets.
The sampling variance of bagged learners is: = [^ ()]Jackknife estimates can be considered to eliminate the bootstrap effects. The jackknife variance estimator is defined as: [1]
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.
Rotation forest – in which every decision tree is trained by first applying principal component analysis (PCA) on a random subset of the input features. [ 13 ] A special case of a decision tree is a decision list , [ 14 ] which is a one-sided decision tree, so that every internal node has exactly 1 leaf node and exactly 1 internal node as a ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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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