Description Usage Arguments Value References Examples
Estimates Boosting of Smooth Trees (BooST)
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x |
Design matrix with explanatory variables. |
y |
Response variable. |
v |
Learning rate (default 0.2). |
p |
Proportion of variables tested in each node split (default 2/3). |
d_max |
Number of splits in each tree (default 4). |
gamma |
Transiction function intensity. Bigger numbers makes the transition less smoth. The default is a sequence of values (0.5:5) to be randomized in each new node. Multiple values may be supplied in a vector to increase the model randomness. |
M |
Number of trees. |
display |
If TRUE, displays iteration counter. |
stochastic |
If TRUE the model will be estimated using Stochasting Gradient Boosting. |
s_prop |
Used only if stochastic=TRUE. Determines the proportion of data used in each tree. |
node_obs |
Equivalent to the minimum number of observations in a termina node for a discrete tree. |
random |
If TRUE trees are grown randomly (default = FALSE) |
An object with S3 class "Boost".
Model |
A list with all trees. |
fitted.values |
Final model fitted values. |
brmse |
Boost rmse in each iteratiob. |
Model |
A list with all trees. |
ybar |
Average value of y used in the first iteration. |
v |
Chosen learning rate. |
rho |
Vector of gradient estimates for each iteration. |
nvar |
Numver of variables in x |
varnames |
colnames of x to be used in other functions. |
params |
Model parameters. |
call |
The matched call. |
blablabla
1 | ## == to be made == ##
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