A model predicts presence of a specific weed using SageMaker built-in linear-learner with predictor_type set to multiclass_classifier. The team wants to minimize false positives. Which hyperparameter change should they make?

To minimize false positives in a multiclass classification problem, you should increase the targetprecision hyperparameter. This hyperparameter is specific to SageMaker's Linear Learner algorithm when predictortype is set to multiclassclassifier. Increasing targetprecision biases the model towards higher precision, meaning it will be more conservative in predicting the positive class, thereby reducing false positives. Setting weightdecay to zero would reduce regularization and could lead to overfitting, not directly addressing false positives. Increasing training epochs might improve overall accuracy but doesn't specifically target false positives. Changing predictortype to regressor would fundamentally alter the model to predict continuous values, which is inappropriate for a classification task.