Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

Please send your paper by e-mail: qQ%R/2w'c[yd7Y.sreHGC|4J+6f]#[iAt=s{_sEL_W#p$bXn=?Lbs@5%S 

Please send 2 files:

(1) the main text without the title of the article and the authors (DOC/DOCX file) and

(2) the title page including: the title of the paper, the authors and their affiliation (DOC/DOCX file).

Please read the detailed editing requirements before submitting your text. 

Number of Results 3

WP(13/2021)361. Machine learning in the prediction of flat horse racing results in Poland

Authors: Borowski Piotr, Chlebus Marcin
Horse racing was the source of many researchers considerations who studied market efficiency and applied complex mathematic formulas to predict their results. We were the first who compared the selected machine learning methods to create a profitable…

WP361. Machine learning in the prediction of flat horse racing results in Poland

Horse racing was the source of many researchers considerations who studied market efficiency and applied complex mathematic formulas to predict their results. We were the first who compared the selected machine learning methods to create a profitable betting strategy for two common bets, Win and Quinella. The six classification algorithms under the different betting scenarios were used, namely Classification and Regression Tree (CART), Generalized Linear Model (Glmnet), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Neural Network (NN) and Linear Discriminant Analysis (LDA). Additionally, the Variable Importance was applied to determine the leading horse racing factors. The data were collected from the flat racetracks in Poland from 2011-2020 and described 3,782 Arabian and Thoroughbred races in total. We managed to profit under specific circumstances and get a correct bets ratio of 41% for the Win bet and over 36% for the Quinella bet using LDA and Neural Networks. The results demonstrated that it was possible to bet effectively using the chosen methods and indicated a possible market inefficiency.


Borowski Piotr, Chlebus Marcin
horse racing prediction racetrack betting Thoroughbred and Arabian flat racing machine learning Variable Importance

WP(18/2020)324. Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling

Authors: Kłosok Marta, Chlebus Marcin
recent years many scientific journals have widely explored the topic of machine learning interpretability. It is important as application of Artificial Intelligence is growing rapidly and its excellent performance is of huge potential for many. There…

WP324. Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling

recent years many scientific journals have widely explored the topic of machine learning interpretability. It is important as application of Artificial Intelligence is growing rapidly and its excellent performance is of huge potential for many. There is also need for overcoming the barriers faced by analysts implementing intelligent systems. The biggest one relates to the problem of explaining why the model made a certain prediction. This work brings the topic of methods for understanding a black-box from both the global and local perspective. Numerous agnostic methods aimed at interpreting black-box model behavior and predictions generated by these complex structures are analyzed. Among them are: Permutation Feature Importance, Partial Dependence Plot, Individual Conditional Expectation Curve, Accumulated Local Effects, techniques approximating predictions of the black-box for single observations with surrogate models (interpretable white-boxes) and Shapley values framework. Our prospect leads toward the question to what extent presented tools enhance model transparency. All of the frameworks are examined in practice with a credit default data use case. The overview presented prove that each of the method has some limitations, but overall almost all summarized techniques produce reliable explanations and contribute to higher transparency accountability of decision systems.


Kłosok Marta, Chlebus Marcin
machine learning explainable Artificial Intelligence visualization techniques model interpretation variable importance

WP(15/2020)321. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools

Authors: Chlebus Marcin, Osika Zuzanna
The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to …

WP321. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools

The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to use boosting algorithms, which have not been used before to predict the response for the campaign and to use Explainable AI (XAI) methods to evaluate model's performance. The paper tries to examine whether 1) complex boosting algorithms perform better and 2) XAI tools are better indicators of models' performance than commonly used discriminatory power's measures like AUC. Portuguese bank telemarketing dataset was used with five machine learning algorithms, namely Random Forest (RF), AdaBoost, GBM, XGBoost and CatBoost, which were then later compared based on their AUC and XAI tools analysis – Permutated Variable Importance and Partial Dependency Profile. Two best performing models based on their AUC were XGBoost and CatBoost, with XGBoost having slightly higher AUC. Then, these models were examined using PDP and VI, which resulted in discovery of XGBoost potenitial overfitting and choosing CatBoost over XGBoost. The results show that new boosting models perform better than older models and that XAI tools could be helpful with models' comparisons.


Chlebus Marcin, Osika Zuzanna
direct marketing telemarketing relationship marketing data mining machine learning random forest adaboost gbm catboost xgboost bank marketing XAI variable importance partial dependency profile
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