Description Usage Arguments Value Examples
Main function for deepboost model creation
1 2 3 | deepboost.default(x, y, instance_weights = NULL, tree_depth = 5,
num_iter = 1, beta = 0, lambda = 0.05, loss_type = "l",
verbose = TRUE)
|
x |
A data.frame of samples' values |
y |
A data.frame of samples's labels |
instance_weights |
The weight of each example |
tree_depth |
maximum depth for a single decision tree in the model |
num_iter |
number of iterations = number of trees in ensemble |
beta |
regularisation for scores (L1) |
lambda |
regularisation for tree depth |
loss_type |
- "l" logistic, "e" exponential |
verbose |
- print extra data while training TRUE / FALSE |
A trained Deepbost model
1 2 3 4 5 | deepboost.default(data.frame(x1=rep(c(0,0,1,1),2),x2=rep(c(0,1,0,1),2)),
factor(rep(c(0,0,0,1),2)),num_iter=1)
deepboost.default(data.frame(x1=rep(c(0,0,1,1),2),x2=rep(c(0,1,0,1),2)),
factor(rep(c(0,0,0,1),2)),
num_iter=2, beta=0.1, lambda=0.00125)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.