treelite
Variables
Model_param

Variables

std::string pred_transform
 name of prediction transform function More...
 
float sigmoid_alpha
 scaling parameter for sigmoid function sigmoid(x) = 1 / (1 + exp(-alpha * x)) More...
 
float global_bias
 global bias of the model More...
 

Detailed Description

Extra parameters for tree ensemble models

Variable Documentation

◆ global_bias

float global_bias

global bias of the model

Predicted margin scores of all instances will be adjusted by the global bias. If unspecified, the bias is set to zero.

Definition at line 399 of file tree.h.

◆ pred_transform

std::string pred_transform

name of prediction transform function

This parameter specifies how to transform raw margin values into final predictions. By default, this is set to 'identity', which means no transformation.

For the multi-class classification task, pred_transfrom must be one of the following values:

- identity_multiclass
do not transform. The output will be a matrix with dimensions
[number of data points] * [number of classes] that contains the margin score
for every (data point, class) pair.
- max_index
compute the most probable class for each data point and output the class
index. The output will be a vector of length [number of data points] that
contains the most likely class of each data point.
- softmax
use the softmax function to transform a multi-dimensional vector into a
proper probability distribution. The output will be a matrix with dimensions
[number of data points] * [number of classes] that contains the predicted
probability of each data point belonging to each class.
- multiclass_ova
apply the sigmoid function element-wise to the margin matrix. The output will
be a matrix with dimensions [number of data points] * [number of classes].

For all other tasks (e.g. regression, binary classification, ranking etc.), pred_transfrom must be one of the following values:

- identity
do not transform. The output will be a vector of length
[number of data points] that contains the margin score for every data point.
- sigmoid
apply the sigmoid function element-wise to the margin vector. The output
will be a vector of length [number of data points] that contains the
probability of each data point belonging to the positive class.
- exponential
apply the exponential function (exp) element-wise to the margin vector. The
output will be a vector of length [number of data points].
- logarithm_one_plus_exp
apply the function f(x) = log(1 + exp(x)) element-wise to the margin vector.
The output will be a vector of length [number of data points].

Definition at line 384 of file tree.h.

◆ sigmoid_alpha

float sigmoid_alpha

scaling parameter for sigmoid function sigmoid(x) = 1 / (1 + exp(-alpha * x))

This parameter is used only when pred_transform is set to 'sigmoid'. It must be strictly positive; if unspecified, it is set to 1.0.

Definition at line 392 of file tree.h.