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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/kernels/boosted_trees/boosted_trees.proto
package org.tensorflow.framework;
/**
*
* DebugOutput contains outputs useful for debugging/model interpretation, at
* the individual example-level. Debug outputs that are available to the user
* are: 1) Directional feature contributions (DFCs) 2) Node IDs for ensemble
* prediction path 3) Leaf node IDs.
*
*
* Protobuf type {@code tensorflow.boosted_trees.DebugOutput}
*/
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DebugOutputOrBuilder {
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
*/
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getFeatureIdsList() {
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
*/
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
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/**
*
* DebugOutput contains outputs useful for debugging/model interpretation, at
* the individual example-level. Debug outputs that are available to the user
* are: 1) Directional feature contributions (DFCs) 2) Node IDs for ensemble
* prediction path 3) Leaf node IDs.
*
*
* Protobuf type {@code tensorflow.boosted_trees.DebugOutput}
*/
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com.google.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.boosted_trees.DebugOutput)
org.tensorflow.framework.DebugOutputOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
*/
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
*/
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*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
*/
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
*/
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
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/**
*
* Return the logits and associated feature splits across prediction paths for
* each tree, for every example, at predict time. We will use these values to
* compute DFCs in Python, by subtracting each child prediction from its
* parent prediction and associating this change with its respective feature
* id.
*
*
* repeated int32 feature_ids = 1;
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