Predicting movie ratings: A comparative study on random forests and support vector machines
Independent thesis Basic level (degree of Bachelor), 15 credits / 22,5 HE creditsStudent thesis
The aim of this work is to evaluate the prediction performance of random forests in comparison to support vector machines, for predicting the numerical user ratings of a movie using pre-release attributes such as its cast, directors, budget and movie genres.
In order to answer this question an experiment was conducted on predicting the overall user rating of 3376 hollywood movies, using data from the well established movie database IMDb. The prediction performance of the two algorithms was assessed and compared over three commonly used performance and error metrics, as well as evaluated by the means of significance testing in order to further investigate whether or not any significant differences could be identified.
The results indicate some differences between the two algorithms, with consistently better performance from random forests in comparison to support vector machines over all of the performance metrics, as well as significantly better results for two out of three metrics. Although a slight difference has been indicated by the results one should also note that both algorithms show great similarities in terms of their prediction performance, making it hard to draw any general conclusions on which algorithm yield the most accurate movie predictions.
Place, publisher, year, edition, pages
2015. , 33 p.
data mining, machine learning, regression, movie prediction, random forests, support vector machines
IdentifiersURN: urn:nbn:se:his:diva-11119OAI: oai:DiVA.org:his-11119DiVA: diva2:821533
Subject / course
Computer Science - Specialization in Systems Development