public com.google.protobuf.ByteString getOptimizationObjectiveBytes() {
    java.lang.Object ref = optimizationObjective_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      optimizationObjective_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }
public com . google . protobuf . ByteString getOptimizationObjectiveBytes ( ) { java . lang . Object ref = optimizationObjective_ ; if ( ref instanceof java . lang . String ) { com . google . protobuf . ByteString b = com . google . protobuf . ByteString . copyFromUtf8 ( ( java . lang . String ) ref ) ; optimizationObjective_ = b ; return b ; } else { return ( com . google . protobuf . ByteString ) ref ; } }
<pre>
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction_type.
If the field is not set, a default objective function is used.
CLASSIFICATION_BINARY:
"MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"MINIMIZE_LOG_LOSS" - Minimize log loss.
"MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
CLASSIFICATION_MULTI_CLASS :
"MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
CLASSIFICATION_MULTI_LABEL:
"MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
REGRESSION:
"MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
"MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
"MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
FORECASTING:
"MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
"MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
</pre>

<code>string optimization_objective = 4;</code>