R: Custom forest

custom_forest {grf} | R Documentation

## Custom forest

### Description

Trains a custom forest model.

### Usage

    custom_forest(X, Y, sample.fraction = 0.5, mtry = NULL,
      num.trees = 2000, num.threads = NULL, min.node.size = NULL,
      honesty = TRUE, honesty.fraction = NULL, alpha = 0.05,
      imbalance.penalty = 0, seed = NULL, clusters = NULL,
      samples_per_cluster = NULL)

### Arguments

`X` | The covariates used in the regression.
`Y` | The outcome.
`sample.fraction` | Fraction of the data used to build each tree. Note: If honesty = TRUE, these subsamples will further be cut by a factor of honesty.fraction.
`mtry` | Number of variables tried for each split.
`num.trees` | Number of trees grown in the forest. Note: Getting accurate confidence intervals generally requires more trees than getting accurate predictions.
`num.threads` | Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.
`min.node.size` | A target for the minimum number of observations in each tree leaf. Note that nodes with size smaller than min.node.size can occur, as in the original randomForest package.
`honesty` | Whether to use honest splitting (i.e., sub-sample splitting).
`honesty.fraction` | The fraction of data that will be used for determining splits if honesty = TRUE. Corresponds to set J1 in the notation of the paper. When using the defaults (honesty = TRUE and honesty.fraction = NULL), half of the data will be used for determining splits
`alpha` | A tuning parameter that controls the maximum imbalance of a split.
`imbalance.penalty` | A tuning parameter that controls how harshly imbalanced splits are penalized.
`seed` | The seed for the C++ random number generator.
`clusters` | Vector of integers or factors specifying which cluster each observation corresponds to.
`samples_per_cluster` | If sampling by cluster, the number of observations to be sampled from each cluster when training a tree. If NULL, we set samples_per_cluster to the size of the smallest cluster. If some clusters are smaller than samples_per_cluster, the whole cluster is used every time the cluster is drawn. Note that clusters with less than samples_per_cluster observations get relatively smaller weight than others in training the forest, i.e., the contribution of a given cluster to the final forest scales with the minimum of the number of observations in the cluster and samples_per_cluster.

### Value

A trained regression forest object.

### Examples

    ## Not run:
    # Train a custom forest.
    n = 50; p = 10
    X = matrix(rnorm(n*p), n, p)
    Y = X[,1] * rnorm(n)
    c.forest = custom_forest(X, Y)

    # Predict using the forest.
    X.test = matrix(0, 101, p)
    X.test[,1] = seq(-2, 2, length.out = 101)
    c.pred = predict(c.forest, X.test)

    ## End(Not run)

[Package grf version 0.10.2 Index]