Application of Diversified Ensemble Learning in Real-life Business Problems: The Case of Predicting Costs of Forwarding Contracts
Date Issued
2022-09-04
Author(s)
Trajanoska, Milena
Gjorgovski, Pavel
Abstract
Finding an optimal machine learning model that
can be applied to a business problem is a complex challenge
that needs to provide a balance between multiple requirements,
including a high predictive performance of the model, continuous
learning and deployment, and explainability of the predictions.
The topic of the FedCSIS 2022 Challenge: ‘Predicting the Costs
of Forwarding Contracts’ is related to the challenges logistics and
transportation companies are facing. To tackle these challenges,
we established an entire Machine Learning framework which
includes domain-specific feature engineering and enrichment,
generic feature transformation and extraction, model hyperparameter tuning, and creating ensembles of traditional and
deep learning models. Our contributions additionally include an
analysis of the types of models which are suitable for the case
of predicting a multi-modal continuous target variable, as well
as an explainable analysis of the features which have the largest
impact on predicting the value of these costs. We further show
that ensembles created by combining multiple different models
trained with different algorithms can improve the performance
on unseen data. In this particular dataset, the experiments
showed that such a combination improves the score by 3%
compared to the best performing individual model.
can be applied to a business problem is a complex challenge
that needs to provide a balance between multiple requirements,
including a high predictive performance of the model, continuous
learning and deployment, and explainability of the predictions.
The topic of the FedCSIS 2022 Challenge: ‘Predicting the Costs
of Forwarding Contracts’ is related to the challenges logistics and
transportation companies are facing. To tackle these challenges,
we established an entire Machine Learning framework which
includes domain-specific feature engineering and enrichment,
generic feature transformation and extraction, model hyperparameter tuning, and creating ensembles of traditional and
deep learning models. Our contributions additionally include an
analysis of the types of models which are suitable for the case
of predicting a multi-modal continuous target variable, as well
as an explainable analysis of the features which have the largest
impact on predicting the value of these costs. We further show
that ensembles created by combining multiple different models
trained with different algorithms can improve the performance
on unseen data. In this particular dataset, the experiments
showed that such a combination improves the score by 3%
compared to the best performing individual model.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
ApplicationofDiversifiedEnsembleLearninginReal-lifeBusinessProblems.pdf
Size
670.25 KB
Format
Adobe PDF
Checksum
(MD5):a87df1819d9d4a72fcd6cd90f63d59c3