Reference documentation and code samples for the Cloud AutoML V1beta1 Client class TablesModelColumnInfo.
An information specific to given column and Tables Model, in context of the Model and the predictions created by it.
Generated from protobuf message google.cloud.automl.v1beta1.TablesModelColumnInfo
Namespace
Google \ Cloud \ AutoMl \ V1beta1Methods
__construct
Constructor.
data
array
Optional. Data for populating the Message object.
↳ column_spec_name
string
Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.
↳ column_display_name
string
Output only. The display name of the column (same as the display_name of its ColumnSpec).
↳ feature_importance
float
Output only. When given as part of a Model (always populated): Measurement of how much model predictions correctness on the TEST data depend on values in this column. A value between 0 and 1, higher means higher influence. These values are normalized - for all input feature columns of a given model they add to 1. When given back by Predict (populated iff feature_importance param is set) or Batch Predict (populated iff feature_importance param is set): Measurement of how impactful for the prediction returned for the given row the value in this column was. Specifically, the feature importance specifies the marginal contribution that the feature made to the prediction score compared to the baseline score. These values are computed using the Sampled Shapley method.
getColumnSpecName
Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.
string
setColumnSpecName
Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.
var
string
$this
getColumnDisplayName
Output only. The display name of the column (same as the display_name of its ColumnSpec).
string
setColumnDisplayName
Output only. The display name of the column (same as the display_name of its ColumnSpec).
var
string
$this
getFeatureImportance
Output only. When given as part of a Model (always populated): Measurement of how much model predictions correctness on the TEST data depend on values in this column. A value between 0 and 1, higher means higher influence. These values are normalized - for all input feature columns of a given model they add to 1.
When given back by Predict (populated iff feature_importance param is set) or Batch Predict (populated iff feature_importance param is set): Measurement of how impactful for the prediction returned for the given row the value in this column was. Specifically, the feature importance specifies the marginal contribution that the feature made to the prediction score compared to the baseline score. These values are computed using the Sampled Shapley method.
float
setFeatureImportance
Output only. When given as part of a Model (always populated): Measurement of how much model predictions correctness on the TEST data depend on values in this column. A value between 0 and 1, higher means higher influence. These values are normalized - for all input feature columns of a given model they add to 1.
When given back by Predict (populated iff feature_importance param is set) or Batch Predict (populated iff feature_importance param is set): Measurement of how impactful for the prediction returned for the given row the value in this column was. Specifically, the feature importance specifies the marginal contribution that the feature made to the prediction score compared to the baseline score. These values are computed using the Sampled Shapley method.
var
float
$this
