A manufacturer has months of sensor telemetry and manual inspection labels in a data lake. They need an automated model that classifies each product as good quality, replacement-market quality, or scrap based on the inspection labels. Which modeling approach is most likely to give the most accurate predictions for this multiclass supervised classification problem?

XGBoost is a powerful and efficient gradient-boosted decision tree algorithm well-suited for tabular data, which sensor telemetry and manual inspection labels typically represent. It excels in supervised classification tasks like this multiclass problem, often providing high accuracy and interpretability. DeepAR is for time-series forecasting, not classification. LDA is an unsupervised topic modeling algorithm, not for supervised classification of product quality. While a CNN (like ResNet) is excellent for image data, it's not the most appropriate or efficient choice for structured, tabular sensor data.