google_api_machine_learning v0.13.0 GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_ExplanationConfig View Source
Message holding configuration options for explaining model predictions. There are two feature attribution methods supported for TensorFlow models: integrated gradients and sampled Shapley. 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: http://proceedings.mlr.press/v70/sundararajan17a.htmlsampledShapleyAttribution
(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.
Link to this section Summary
Functions
Unwrap a decoded JSON object into its complex fields.
Link to this section Types
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t()
View Sourcet() :: %GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_ExplanationConfig{ integratedGradientsAttribution: GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_IntegratedGradientsAttribution.t(), sampledShapleyAttribution: GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_SampledShapleyAttribution.t(), xraiAttribution: GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1_XraiAttribution.t() }
Link to this section Functions
Unwrap a decoded JSON object into its complex fields.