Trains a boosted regression forest that can be used to estimate the conditional mean function mu(x) = E[Y | X = x]. Selects number of boosting iterations based on cross-validation.
boosted_regression_forest( X, Y, num.trees = 2000, sample.weights = NULL, clusters = NULL, equalize.cluster.weights = FALSE, sample.fraction = 0.5, mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)), min.node.size = 5, honesty = TRUE, honesty.fraction = 0.5, honesty.prune.leaves = TRUE, alpha = 0.05, imbalance.penalty = 0, ci.group.size = 2, tune.parameters = "none", tune.num.trees = 10, tune.num.reps = 100, tune.num.draws = 1000, boost.steps = NULL, boost.error.reduction = 0.97, boost.max.steps = 5, boost.trees.tune = 10, num.threads = NULL, seed = runif(1, 0, .Machine$integer.max) )
| X | The covariates used in the regression. |
|---|---|
| Y | The outcome. |
| num.trees | Number of trees grown in the forest. Note: Getting accurate confidence intervals generally requires more trees than getting accurate predictions. Default is 2000. |
| sample.weights | Weights given to each observation in estimation. If NULL, each observation receives the same weight. Default is NULL. |
| clusters | Vector of integers or factors specifying which cluster each observation corresponds to. Default is NULL (ignored). |
| equalize.cluster.weights | If FALSE, each unit is given the same weight (so that bigger clusters get more weight). If TRUE, each cluster is given equal weight in the forest. In this case, during training, each tree uses the same number of observations from each drawn cluster: If the smallest cluster has K units, then when we sample a cluster during training, we only give a random K elements of the cluster to the tree-growing procedure. When estimating average treatment effects, each observation is given weight 1/cluster size, so that the total weight of each cluster is the same. Note that, if this argument is FALSE, sample weights may also be directly adjusted via the sample.weights argument. If this argument is TRUE, sample.weights must be set to NULL. Default is FALSE. |
| 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. Default is 0.5. |
| mtry | Number of variables tried for each split. Default is \(\sqrt p + 20\) where p is the number of variables. |
| 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. Default is 5. |
| honesty | Whether to use honest splitting (i.e., sub-sample splitting). Default is TRUE. For a detailed description of honesty, honesty.fraction, honesty.prune.leaves, and recommendations for parameter tuning, see the grf algorithm reference. |
| 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. Default is 0.5 (i.e. half of the data is used for determining splits). |
| honesty.prune.leaves | If TRUE, prunes the estimation sample tree such that no leaves are empty. If FALSE, keep the same tree as determined in the splits sample (if an empty leave is encountered, that tree is skipped and does not contribute to the estimate). Setting this to FALSE may improve performance on small/marginally powered data, but requires more trees (note: tuning does not adjust the number of trees). Only applies if honesty is enabled. Default is TRUE. |
| alpha | A tuning parameter that controls the maximum imbalance of a split. Default is 0.05. |
| imbalance.penalty | A tuning parameter that controls how harshly imbalanced splits are penalized. Default is 0. |
| ci.group.size | The forest will grow ci.group.size trees on each subsample. In order to provide confidence intervals, ci.group.size must be at least 2. Default is 2. |
| tune.parameters | If true, NULL parameters are tuned by cross-validation; if FALSE NULL parameters are set to defaults. Default is FALSE. |
| tune.num.trees | The number of trees in each 'mini forest' used to fit the tuning model. Default is 10. |
| tune.num.reps | The number of forests used to fit the tuning model. Default is 100. |
| tune.num.draws | The number of random parameter values considered when using the model to select the optimal parameters. Default is 1000. |
| boost.steps | The number of boosting iterations. If NULL, selected by cross-validation. Default is NULL. |
| boost.error.reduction | If boost.steps is NULL, the percentage of previous steps' error that must be estimated by cross validation in order to take a new step, default 0.97. |
| boost.max.steps | The maximum number of boosting iterations to try when boost.steps=NULL. Default is 5. |
| boost.trees.tune | If boost.steps is NULL, the number of trees used to test a new boosting step when tuning boost.steps. Default is 10. |
| num.threads | Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount. |
| seed | The seed for the C++ random number generator. |
A boosted regression forest object. $error contains the mean debiased error for each step, and $forests contains the trained regression forest for each step.
# \donttest{ # Train a boosted regression forest. n <- 50 p <- 10 X <- matrix(rnorm(n * p), n, p) Y <- X[, 1] * rnorm(n) boosted.forest <- boosted_regression_forest(X, Y) # Predict using the forest. X.test <- matrix(0, 101, p) X.test[, 1] <- seq(-2, 2, length.out = 101) boost.pred <- predict(boosted.forest, X.test) # Predict on out-of-bag training samples. boost.pred <- predict(boosted.forest) # Check how many boosting iterations were used print(length(boosted.forest$forests))#> [1] 1# }