Description Usage Arguments Value See Also Examples
Random forest is a technique for reducing the variance of an estimated prediction function. It takes multiple random samples(with replacement) from the training data set, uses each of these samples to construct a separate model and separate predictions for test set, and then averages them.
1 |
data |
FLTable |
formula |
formula specifying the independent and dependent variable columns |
ntree |
Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. |
mtry |
Number of variables randomly sampled as candidates at each split |
nodesize |
Minimum size of terminal nodes. |
maxdepth |
The maximum depth to which the tree can go. cp: Complexity parameter |
An object of class "FLRandomForest" containing the forest structure details.
randomForest
for corresponding R function reference.
1 2 3 4 5 6 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.