Cloud AutoML V1beta1 Client - Class TablesModelColumnInfo (1.6.5)

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 \ V1beta1

Methods

__construct

Constructor.

Parameters
Name Description
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.

Returns
Type Description
string

setColumnSpecName

Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.

Parameter
Name Description
var string
Returns
Type Description
$this

getColumnDisplayName

Output only. The display name of the column (same as the display_name of its ColumnSpec).

Returns
Type Description
string

setColumnDisplayName

Output only. The display name of the column (same as the display_name of its ColumnSpec).

Parameter
Name Description
var string
Returns
Type Description
$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.

Returns
Type Description
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.

Parameter
Name Description
var float
Returns
Type Description
$this