View Source GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_ExplanationConfig (google_api_machine_learning v0.28.1)
Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. Learn more about feature attributions.
Attributes
-
integratedGradientsAttribution
(type:GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_IntegratedGradientsAttribution.t
, default:nil
) - Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 -
sampledShapleyAttribution
(type:GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_SampledShapleyAttribution.t
, default:nil
) - An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. -
xraiAttribution
(type:GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_XraiAttribution.t
, default:nil
) - Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.
Summary
Functions
Unwrap a decoded JSON object into its complex fields.
Types
@type t() :: %GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_ExplanationConfig{ integratedGradientsAttribution: GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_IntegratedGradientsAttribution.t() | nil, sampledShapleyAttribution: GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_SampledShapleyAttribution.t() | nil, xraiAttribution: GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_XraiAttribution.t() | nil }
Functions
Unwrap a decoded JSON object into its complex fields.