Starting April 29, 2025, Gemini 1.5 Pro and Gemini 1.5 Flash models are not available in projects that have no prior usage of these models, including new projects. For details, see Model versions and lifecycle.
Output only. Model predicted output if the input instance is constructed
from the baselines of all the features defined in
ExplanationMetadata.inputs.
The field name of the output is determined by the key in
ExplanationMetadata.outputs.
If the Model's predicted output has multiple dimensions (rank > 1), this is
the value in the output located by
output_index.
If there are multiple baselines, their output values are averaged.
Output only. Attributions of each explained feature. Features are extracted
from the prediction
instances according
to explanation metadata for
inputs.
The value is a struct, whose keys are the name of the feature. The values
are how much the feature in the
instance contributed
to the predicted result.
The format of the value is determined by the feature's input format:
If the feature is a scalar value, the attribution value is a
floating number.
If the feature is an array of scalar values, the attribution value is
an array.
If the feature is a struct, the attribution value is a
struct. The keys in the
attribution value struct are the same as the keys in the feature
struct. The formats of the values in the attribution struct are
determined by the formats of the values in the feature struct.
The
ExplanationMetadata.feature_attributions_schema_uri
field, pointed to by the
ExplanationSpec field of the
Endpoint.deployed_models
object, points to the schema file that describes the features and their
attribution values (if it is populated).
Output only. Attributions of each explained feature. Features are extracted
from the prediction
instances according
to explanation metadata for
inputs.
The value is a struct, whose keys are the name of the feature. The values
are how much the feature in the
instance contributed
to the predicted result.
The format of the value is determined by the feature's input format:
If the feature is a scalar value, the attribution value is a
floating number.
If the feature is an array of scalar values, the attribution value is
an array.
If the feature is a struct, the attribution value is a
struct. The keys in the
attribution value struct are the same as the keys in the feature
struct. The formats of the values in the attribution struct are
determined by the formats of the values in the feature struct.
The
ExplanationMetadata.feature_attributions_schema_uri
field, pointed to by the
ExplanationSpec field of the
Endpoint.deployed_models
object, points to the schema file that describes the features and their
attribution values (if it is populated).
Output only. Model predicted output on the corresponding explanation
instance. The field name of the output is
determined by the key in
ExplanationMetadata.outputs.
If the Model predicted output has multiple dimensions, this is the value in
the output located by
output_index.
Output only. The display name of the output identified by
output_index. For
example, the predicted class name by a multi-classification Model.
This field is only populated iff the Model predicts display names as a
separate field along with the explained output. The predicted display name
must has the same shape of the explained output, and can be located using
output_index.
Output only. The display name of the output identified by
output_index. For
example, the predicted class name by a multi-classification Model.
This field is only populated iff the Model predicts display names as a
separate field along with the explained output. The predicted display name
must has the same shape of the explained output, and can be located using
output_index.
Output only. The index that locates the explained prediction output.
If the prediction output is a scalar value, output_index is not populated.
If the prediction output has multiple dimensions, the length of the
output_index list is the same as the number of dimensions of the output.
The i-th element in output_index is the element index of the i-th dimension
of the output vector. Indices start from 0.
Output only. The index that locates the explained prediction output.
If the prediction output is a scalar value, output_index is not populated.
If the prediction output has multiple dimensions, the length of the
output_index list is the same as the number of dimensions of the output.
The i-th element in output_index is the element index of the i-th dimension
of the output vector. Indices start from 0.
Output only. The index that locates the explained prediction output.
If the prediction output is a scalar value, output_index is not populated.
If the prediction output has multiple dimensions, the length of the
output_index list is the same as the number of dimensions of the output.
The i-th element in output_index is the element index of the i-th dimension
of the output vector. Indices start from 0.
Output only. Attributions of each explained feature. Features are extracted
from the prediction
instances according
to explanation metadata for
inputs.
The value is a struct, whose keys are the name of the feature. The values
are how much the feature in the
instance contributed
to the predicted result.
The format of the value is determined by the feature's input format:
If the feature is a scalar value, the attribution value is a
floating number.
If the feature is an array of scalar values, the attribution value is
an array.
If the feature is a struct, the attribution value is a
struct. The keys in the
attribution value struct are the same as the keys in the feature
struct. The formats of the values in the attribution struct are
determined by the formats of the values in the feature struct.
The
ExplanationMetadata.feature_attributions_schema_uri
field, pointed to by the
ExplanationSpec field of the
Endpoint.deployed_models
object, points to the schema file that describes the features and their
attribution values (if it is populated).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-03 UTC."],[],[],null,["# Interface AttributionOrBuilder (1.32.0)\n\n public interface AttributionOrBuilder extends MessageOrBuilder\n\nImplements\n----------\n\n[MessageOrBuilder](https://cloud.google.com/java/docs/reference/protobuf/latest/com.google.protobuf.MessageOrBuilder.html)\n\nMethods\n-------\n\n### getApproximationError()\n\n public abstract double getApproximationError()\n\nOutput only. Error of\nfeature_attributions\ncaused by approximation used in the explanation method. Lower value means\nmore precise attributions.\n\n- For Sampled Shapley attribution, increasing path_count might reduce the error.\n- For Integrated Gradients attribution, increasing step_count might reduce the error.\n- For XRAI attribution,\n increasing\n step_count might\n reduce the error.\n\n See [this introduction](/vertex-ai/docs/explainable-ai/overview)\n for more information.\n\n`double approximation_error = 6 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getBaselineOutputValue()\n\n public abstract double getBaselineOutputValue()\n\nOutput only. Model predicted output if the input instance is constructed\nfrom the baselines of all the features defined in\nExplanationMetadata.inputs.\nThe field name of the output is determined by the key in\nExplanationMetadata.outputs.\n\nIf the Model's predicted output has multiple dimensions (rank \\\u003e 1), this is\nthe value in the output located by\noutput_index.\n\nIf there are multiple baselines, their output values are averaged.\n\n`double baseline_output_value = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getFeatureAttributions()\n\n public abstract Value getFeatureAttributions()\n\nOutput only. Attributions of each explained feature. Features are extracted\nfrom the prediction instances according\nto explanation metadata for inputs.\n\nThe value is a struct, whose keys are the name of the feature. The values\nare how much the feature in the\ninstance contributed\nto the predicted result.\n\nThe format of the value is determined by the feature's input format:\n\n- If the feature is a scalar value, the attribution value is a\n floating number.\n\n- If the feature is an array of scalar values, the attribution value is\n an array.\n\n- If the feature is a struct, the attribution value is a\n struct. The keys in the\n attribution value struct are the same as the keys in the feature\n struct. The formats of the values in the attribution struct are\n determined by the formats of the values in the feature struct.\n\n The\n ExplanationMetadata.feature_attributions_schema_uri\n field, pointed to by the\n ExplanationSpec field of the\n Endpoint.deployed_models\n object, points to the schema file that describes the features and their\n attribution values (if it is populated).\n\n`\n.google.protobuf.Value feature_attributions = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];\n`\n\n### getFeatureAttributionsOrBuilder()\n\n public abstract ValueOrBuilder getFeatureAttributionsOrBuilder()\n\nOutput only. Attributions of each explained feature. Features are extracted\nfrom the prediction instances according\nto explanation metadata for inputs.\n\nThe value is a struct, whose keys are the name of the feature. The values\nare how much the feature in the\ninstance contributed\nto the predicted result.\n\nThe format of the value is determined by the feature's input format:\n\n- If the feature is a scalar value, the attribution value is a\n floating number.\n\n- If the feature is an array of scalar values, the attribution value is\n an array.\n\n- If the feature is a struct, the attribution value is a\n struct. The keys in the\n attribution value struct are the same as the keys in the feature\n struct. The formats of the values in the attribution struct are\n determined by the formats of the values in the feature struct.\n\n The\n ExplanationMetadata.feature_attributions_schema_uri\n field, pointed to by the\n ExplanationSpec field of the\n Endpoint.deployed_models\n object, points to the schema file that describes the features and their\n attribution values (if it is populated).\n\n`\n.google.protobuf.Value feature_attributions = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];\n`\n\n### getInstanceOutputValue()\n\n public abstract double getInstanceOutputValue()\n\nOutput only. Model predicted output on the corresponding explanation instance. The field name of the output is\ndetermined by the key in\nExplanationMetadata.outputs.\n\nIf the Model predicted output has multiple dimensions, this is the value in\nthe output located by\noutput_index.\n\n`double instance_output_value = 2 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getOutputDisplayName()\n\n public abstract String getOutputDisplayName()\n\nOutput only. The display name of the output identified by\noutput_index. For\nexample, the predicted class name by a multi-classification Model.\n\nThis field is only populated iff the Model predicts display names as a\nseparate field along with the explained output. The predicted display name\nmust has the same shape of the explained output, and can be located using\noutput_index.\n\n`string output_display_name = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getOutputDisplayNameBytes()\n\n public abstract ByteString getOutputDisplayNameBytes()\n\nOutput only. The display name of the output identified by\noutput_index. For\nexample, the predicted class name by a multi-classification Model.\n\nThis field is only populated iff the Model predicts display names as a\nseparate field along with the explained output. The predicted display name\nmust has the same shape of the explained output, and can be located using\noutput_index.\n\n`string output_display_name = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getOutputIndex(int index)\n\n public abstract int getOutputIndex(int index)\n\nOutput only. The index that locates the explained prediction output.\n\nIf the prediction output is a scalar value, output_index is not populated.\nIf the prediction output has multiple dimensions, the length of the\noutput_index list is the same as the number of dimensions of the output.\nThe i-th element in output_index is the element index of the i-th dimension\nof the output vector. Indices start from 0.\n\n`repeated int32 output_index = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getOutputIndexCount()\n\n public abstract int getOutputIndexCount()\n\nOutput only. The index that locates the explained prediction output.\n\nIf the prediction output is a scalar value, output_index is not populated.\nIf the prediction output has multiple dimensions, the length of the\noutput_index list is the same as the number of dimensions of the output.\nThe i-th element in output_index is the element index of the i-th dimension\nof the output vector. Indices start from 0.\n\n`repeated int32 output_index = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getOutputIndexList()\n\n public abstract List\u003cInteger\u003e getOutputIndexList()\n\nOutput only. The index that locates the explained prediction output.\n\nIf the prediction output is a scalar value, output_index is not populated.\nIf the prediction output has multiple dimensions, the length of the\noutput_index list is the same as the number of dimensions of the output.\nThe i-th element in output_index is the element index of the i-th dimension\nof the output vector. Indices start from 0.\n\n`repeated int32 output_index = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getOutputName()\n\n public abstract String getOutputName()\n\nOutput only. Name of the explain output. Specified as the key in\nExplanationMetadata.outputs.\n\n`string output_name = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### getOutputNameBytes()\n\n public abstract ByteString getOutputNameBytes()\n\nOutput only. Name of the explain output. Specified as the key in\nExplanationMetadata.outputs.\n\n`string output_name = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];`\n\n### hasFeatureAttributions()\n\n public abstract boolean hasFeatureAttributions()\n\nOutput only. Attributions of each explained feature. Features are extracted\nfrom the prediction instances according\nto explanation metadata for inputs.\n\nThe value is a struct, whose keys are the name of the feature. The values\nare how much the feature in the\ninstance contributed\nto the predicted result.\n\nThe format of the value is determined by the feature's input format:\n\n- If the feature is a scalar value, the attribution value is a\n floating number.\n\n- If the feature is an array of scalar values, the attribution value is\n an array.\n\n- If the feature is a struct, the attribution value is a\n struct. The keys in the\n attribution value struct are the same as the keys in the feature\n struct. The formats of the values in the attribution struct are\n determined by the formats of the values in the feature struct.\n\n The\n ExplanationMetadata.feature_attributions_schema_uri\n field, pointed to by the\n ExplanationSpec field of the\n Endpoint.deployed_models\n object, points to the schema file that describes the features and their\n attribution values (if it is populated).\n\n`\n.google.protobuf.Value feature_attributions = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];\n`"]]