Description Usage Arguments Details Value References See Also
Functions to get "learner" functions for gpe
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  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. 
remove_duplicates_complements 

mtry 
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 
ntrees 
Number of trees to fit. Will not have an effect if 
maxdepth 
Maximum depth of trees. Will not have an effect if 
learnrate 
Learning rate for methods. Corresponds to the ν parameter in Friedman & Popescu (2008). 
parallel 

use_grad 

tree.control 

winsfrac 
Quantile to winsorize linear terms. The value should be in [0,0.5) 
normalize 

degree 
Maximum degree of interactions in 
nk 
Maximum number of basis functions in 
ntrain 
Number of models to fit. 
cor_thresh 
A threshold on the pairwise correlation for removal of basis functions. This is similar to 
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
.
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
Hothorn, T., & Zeileis, A. (2015). partykit: A modular toolkit for recursive partytioning in R. Journal of Machine Learning Research, 16, 39053909.
Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals Statistics, 19(1), 167.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Applied Statistics, 29(5), 11891232.
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), 916954.
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.
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