A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner…

The primary goal is to minimize false positives, which are instances incorrectly predicted as positive (e.g., predicting a weed is present when it is not). This is equivalent to maximizing the precision metric, defined as True Positives / (True Positives + False Positives). To increase the precision score, the model must reduce the number of false positives. The Amazon SageMaker Linear Learner algorithm provides hyperparameters to optimize the model's classification threshold for specific metrics. By increasing the value of a precision-oriented hyperparameter, the training process is explicitly guided to favor a model that makes fewer false positive errors.

A. Set the value of the weight decay hyperparameter to zero: This removes L2 regularization, which can increase the risk of overfitting. It is a general model complexity control, not a direct tool for minimizing false positives.

B. Increase the number of training epochs: Increasing epochs can lead to better model convergence but also risks overfitting. It does not specifically target the trade-off between precision and recall to reduce false positives.

D. Change the value of the predictortype hyperparameter to regressor: This changes the problem from classification (predicting a category) to regression (predicting a continuous value), making the concept of false positives inapplicable.

1. Amazon SageMaker Developer Guide: The Linear Learner documentation details hyperparameters for optimizing the model. For classification

the model can be optimized for metrics like precision. The guide states

"For the binaryclassifier predictor type

you can also optimize the model for a specific metric (precision

recall

fbeta) by setting the binaryclassifiermodelselectioncriteria hyperparameter." While the question specifies multiclassclassifier

the principle of optimizing for precision to reduce false positives is a core

intended mechanism presented in the algorithm's capabilities.

Source: Amazon SageMaker Developer Guide

"Linear Learner Algorithm

" section on "Optimization Hyperparameters."

2. Stanford University CS229 Course Notes: Course materials on machine learning evaluation metrics define precision and its relationship to false positives. "Precision measures

of all the examples the classifier labeled as positive

what fraction were actually positive... A model with high precision will have a low number of false positives."

Source: Stanford University

CS229 Machine Learning

"Evaluation Metrics" by Andrew Ng. (Specifically

discussions on Precision-Recall).

3. Peer-Reviewed Publication: In "The Elements of Statistical Learning

" the concepts of Type I (false positive) and Type II (false negative) errors are fundamental. Optimizing for precision is a standard strategy to control the rate of Type I errors.

Source: Hastie

T.

Tibshirani

R.

& Friedman

J. (2009). The Elements of Statistical Learning: Data Mining

Inference

and Prediction. Springer. (See Chapter 7

"Model Assessment and Selection").