Functions to get "learner" functions for gpe.
gpe_trees(..., remove_duplicates_complements = TRUE, mtry = Inf,
ntrees = 500, maxdepth = 3L, learnrate = 0.01, parallel = FALSE,
use_grad = TRUE, tree.control = ctree_control(mtry = mtry, maxdepth =
maxdepth))gpe_linear(..., winsfrac = 0.025, normalize = TRUE)
gpe_earth(..., degree = 3, nk = 8, normalize = TRUE, ntrain = 100,
learnrate = 0.1, cor_thresh = 0.99)
Currently not used.
TRUE. Should rules with complementary or duplicate support be removed?
Number of input variables randomly sampled as candidates at each node for random forest like algorithms. The argument is passed to the tree methods in the partykit package.
Number of trees to fit. Will not have an effect if tree.control is used.
Maximum depth of trees. Will not have an effect if tree.control is used.
Learning rate for methods. Corresponds to the \(\nu\) parameter in Friedman & Popescu (2008).
TRUE. Should basis functions be found in parallel?
ctree_control with options for the ctree function.
Quantile to winsorize linear terms. The value should be in \([0,0.5)\)
TRUE. Should value be scaled by .4 times the inverse standard deviation? If TRUE, gives linear terms the same influence as a typical rule.
Maximum degree of interactions in earth model.
Maximum number of basis functions in earth model.
Number of models to fit.
A threshold on the pairwise correlation for removal of basis functions. This is similar to remove_duplicates_complements. One of the basis functions in pairs where the correlation exceeds the threshold is excluded. NULL implies no exclusion. Setting a value closer to zero will decrease the time needed to fit the final model.
A function that has formal arguments formula, data, weights, sample_func, verbose, family, .... The function returns a vector with character where each element is a term for the final formula in the call to cv.glmnet
gpe_trees provides learners for tree method. Either ctree or glmtree from the partykit package will be used.
gpe_linear provides linear terms for the gpe.
gpe_earth provides basis functions where each factor is a hinge function. The model is estimated with earth.
Hothorn, T., & Zeileis, A. (2015). partykit: A modular toolkit for recursive partytioning in R. Journal of Machine Learning Research, 16, 3905-3909.
Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals Statistics, 19(1), 1-67.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Applied Statistics, 29(5), 1189-1232.
Friedman, J. H. (1993). Fast MARS. Dept. of Statistics Technical Report No. 110, Stanford University.
Friedman, J. H., & Popescu, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916-954.
Chen T., & Guestrin C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.