Model parameters are used by ModelBuilder
class to
clarify certain behaviors of a tree ensemble model.
pred_transform
[TREELITE_MAX_PRED_TRANSFORM_LENGTH
] = {0}¶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].
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.
- signed_square
Apply the function f(x) = sign(x) * (x**2) element-wise to the margin vector. The
output will be a vector of length [number of data points].
- hinge
Apply the function f(x) = (1 if x > 0 else 0) element-wise to the margin vector. The
output will be a vector of length [number of data points], filled with 0's and 1's.
- 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].
- exponential_standard_ratio
Apply the exponential base 2 function (exp2) element-wise to a standardized
version of the margin vector. The output will be a vector of length [number of data points].
Each output element is exp2(-x / c), where x is the margin and c is the standardization constant.
- 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].
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.
ratio_c
¶scaling parameter for exponential standard ratio transformation expstdratio(x) = exp2(-x / c)
This parameter is used only when pred_transform
is set to 'exponential_standard_ratio'
. If unspecified, it is set to 1.0.
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.