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.