XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post-processing the predictions. References:

XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.

XGBoost Parameters: A reference guide for the parameters of XGBoost.