Interface TablesModelColumnInfoOrBuilder (2.3.10)

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public interface TablesModelColumnInfoOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getColumnDisplayName()

public abstract String getColumnDisplayName()

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

string column_display_name = 2;

Returns
TypeDescription
String

The columnDisplayName.

getColumnDisplayNameBytes()

public abstract ByteString getColumnDisplayNameBytes()

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

string column_display_name = 2;

Returns
TypeDescription
ByteString

The bytes for columnDisplayName.

getColumnSpecName()

public abstract String getColumnSpecName()

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

string column_spec_name = 1;

Returns
TypeDescription
String

The columnSpecName.

getColumnSpecNameBytes()

public abstract ByteString getColumnSpecNameBytes()

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

string column_spec_name = 1;

Returns
TypeDescription
ByteString

The bytes for columnSpecName.

getFeatureImportance()

public abstract float 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 feature_importance = 3;

Returns
TypeDescription
float

The featureImportance.