Compute and return a vector representation of a forest's leaf predictions for every observation in a dataset. The vector has a "column-major" format that can be easily re-represented as as a CSC sparse matrix: elements are organized so that the first n elements correspond to leaf predictions for all n observations in a dataset for the first tree in an ensemble, the next n elements correspond to predictions for the second tree and so on. The "data" for each element corresponds to a uniquely mapped column index that corresponds to a single leaf of a single tree (i.e. if tree 1 has 3 leaves, its column indices range from 0 to 2, and then tree 2's leaf indices begin at 3, etc...). Users may pass a single dataset (which we refer to here as a "training set") or two datasets (which we refer to as "training and test sets"). This verbiage hints that one potential use-case for a matrix of leaf indices is to define a ensemble-based kernel for kriging.
computeForestLeafIndices.RdCompute and return a vector representation of a forest's leaf predictions for
every observation in a dataset.
The vector has a "column-major" format that can be easily re-represented as
as a CSC sparse matrix: elements are organized so that the first n elements
correspond to leaf predictions for all n observations in a dataset for the
first tree in an ensemble, the next n elements correspond to predictions for
the second tree and so on. The "data" for each element corresponds to a uniquely
mapped column index that corresponds to a single leaf of a single tree (i.e.
if tree 1 has 3 leaves, its column indices range from 0 to 2, and then tree 2's
leaf indices begin at 3, etc...).
Users may pass a single dataset (which we refer to here as a "training set")
or two datasets (which we refer to as "training and test sets"). This verbiage
hints that one potential use-case for a matrix of leaf indices is to define a
ensemble-based kernel for kriging.
Arguments
- bart_model
Object of type
bartmodelcorresponding to a BART model with at least one sample- X_train
Matrix of "training" data. In a traditional Gaussian process kriging context, this corresponds to the observations for which outcomes are observed.
- X_test
(Optional) Matrix of "test" data. In a traditional Gaussian process kriging context, this corresponds to the observations for which outcomes are unobserved and must be estimated based on the kernels k(X_test,X_test), k(X_test,X_train), and k(X_train,X_train). If not provided, this function will only compute k(X_train, X_train).
- forest_num
(Option) Index of the forest sample to use for kernel computation. If not provided, this function will use the last forest.