You have a labeled dataset of 1,200 products, each described by 15 numeric features (title, dimensions, weight, price, etc.) and assigned to one of six categorical product classes. Which model is most appropriate to train for classifying new products using this dataset?

XGBoost is a powerful, tree-based ensemble method well-suited for structured tabular data like this, where features are numeric and the target is categorical. Configuring it for multi:softmax directly handles multiclass classification. Deep CNNs are primarily designed for image data, not tabular data, and would be an inefficient choice here. A regression forest predicts continuous values, not discrete categories, making it unsuitable for classification. DeepAR is a forecasting model for time series data, which is irrelevant to this product classification task.