Exploring Supervised Learning Methods for Predicting Cuisines from Their Ingredients

Various regions around the world use similar ingredients for food preparation, with exceptions of unique regional ingredients. However, the variation in the cuisines in the regions stems from the unique combinations of these ingredients. This aspect has been explored in Kaggle's competition, in which many submissions and solutions have been put forward. However, to the best of our knowledge, there is still no paper that compares Backpropagation, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, and AdaBoost to predict cuisines based on their ingredients. We present our approach and measurement of those Supervised Learning Methods for tackling the problem. We use a combination of Machine Learning library and our own method implementations to conduct the experiment. Our results show that all the methods have more than 55% accuracy, and the best result achieved is 76.769% for Support Vector Machine. Given the small data size and high dimensionality of text data, SVM and Naive Bayes generalize well, compared to the more complex methods such as Neural Network. Our results also suggest that Random forest is robust and handles noise in the data well compared to AdaBoost.