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A Comparative Performance Study of Naïve and Ensemble Algorithms for E-commerce

The research work introduced in this paper focuses on providing a supporting layer of algorithms to work with the available information provided by producers in e-commerce; more specifically, we focus on the groceries sector. The main objective is comparing different machine learning algorithms in terms of positioning accuracy and computation time to decide the most appropriate ones to classify products into a given taxonomy of groceries used in the foods & beverage industry. In experimental result, we have used dataset provided by a Midiadia, a Spanish company that tries to enrich the vendors’ catalogs by including the information of each product given by producers. We focused our work in two types of models: (i) Naïve algorithms, like Decision tree, Logistic Regression, SVM and K-NN, and (ii) Ensemble algorithms, like Boosted, Bagged Trees, RUS, Subspace Discriminant and K-NN. The latter offers better results and, more specifically, Boosted Trees give the best performance measures.