Model Parameters

Model parameters are used by ModelBuilder class to clarify certain behaviors of a tree ensemble model.

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char 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].

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.
  - 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].

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.

float 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.

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.