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.
  - 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].

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 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.