An Approach for Predicting the Costs of Forwarding Contracts using Gradient Boosting

Abstract

Predicting the cost of forwarding contract is a severe challenge to road transport management system. The transportation cost of a forwarding contract often depends on many factors. It is hard for humans to evaluate the various factors in transportation and calculate the cost of forwarding contract. In this paper, we propose an approach to address such a problem by following the sequence of machine learning steps which consist of data analysis, feature engineering and model construction. First, we conduct a detailed analysis of the given data. Then, we generate effective features to characterize the cost of forwarding contract and eliminate redundant features. Finally, in the model construction phase, we propose a gradient boosting decision tree based method to train and predict the cost of forwarding contract. The proposed approach achieves RMSE scores of 0.1391 on the test set, which is the 2 nd final score in the competition.

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

“…We use this competition as an illustration how KnowledgePit works, as well as a prerequisite for the discussion in Section V. We refer to [7] for more details about this particular contest. The best solutions submitted by its participants are described in [23] (1st place), [24] (2nd), [25] (3rd), and [26] (4th).…”

“…Table II shows the results for the obtained ensembles. As a reference, we include the results obtained using simple averaging of the best solutions from top 5 teams (solutions [23], [24], [25], [26] and the baseline model), and the results of weighted averaging in which the weights correspond to the preliminary scores. We also include results obtained for the simple averaging, and weighted averaging when the number of used solutions corresponds to the optimal selection of the λ value.…”