View source: R/logic.boosting.R
logicDT.boosting | R Documentation |
Function for fitting gradient boosted logicDT models.
## Default S3 method: logicDT.boosting( X, y, Z = NULL, boosting.iter = 500, learning.rate = 0.01, subsample.frac = 1, replace = TRUE, line.search = "min", ... ) ## S3 method for class 'formula' logicDT.boosting(formula, data, ...)
X |
Matrix or data frame of binary predictors coded as 0 or 1. |
y |
Response vector. 0-1 coding for binary responses. Otherwise, a regression task is assumed. |
Z |
Optional matrix or data frame of quantitative/continuous covariables. Multiple covariables allowed for splitting the trees. If four parameter logistic models shall be fitted in the leaves, only the first given covariable is used. |
boosting.iter |
Number of boosting iterations |
learning.rate |
Learning rate for boosted models. Values between 0.001 and 0.1 are recommended. |
subsample.frac |
Subsample fraction for each boosting iteration. E.g., 0.5 means that are random draw of 50 is used in each iteration. |
replace |
Should the random draws with subsample.frac in boosted models be performed with or without replacement? TRUE or FALSE |
line.search |
Type of line search for gradient boosting. "min" performs a real minimization while "binary" performs a loose binary search for a boosting coefficient that just reduces the score. |
... |
Arguments passed to |
formula |
An object of type |
data |
A data frame containing the data for the corresponding
|
Details on single logicDT models can be found in logicDT
.
An object of class logic.boosted
. This is a list
containing
|
A list of fitted logicDT models |
|
A vector of boosting coefficient corresponding to each model |
|
Initial model which is usually the observed mean |
|
Supplied parameters of the functional call
to |
Lau, M., Schikowski, T. & Schwender, H. (2021). logicDT: A Procedure for Identifying Response-Associated Interactions Between Binary Predictors. To be submitted.
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232. doi: 10.1214/aos/1013203451
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