A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results
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19. März 2025
Über diesen Artikel
Online veröffentlicht: 19. März 2025
Seitenbereich: 56 - 72
DOI: https://doi.org/10.2478/ijcss-2025-0004
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© 2025 Messaoud Bendiaf et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Description of features_
| HomeTeam | Home Team |
| AwayTeam | Away Team |
| FTR | Full Time Result (H=Home Win, D=Draw, A=Away Win) |
| HS | Home Team Shots |
| AS | Away Team Shots |
| HST | Home Team Shots on Target |
| AST | Away Team Shots on Target |
| HC | Home Team Corners |
| AC | Away Team Corners |
| HF | Home Team Fouls Committed |
| AF | Away Team Fouls Committed |
| HY | Home Team Yellow Cards |
| AY | Away Team Yellow Cards |
| HR | Home Team Red Cards |
| AR | Away Team Red Cards |
Performance comparison of the six models in our study_
| Logistic Regression | 0.64 | 0.62 | 0.64 | 0.63 | 0.81 |
| Naïve Bayes | 0.60 | 0.60 | 0.60 | 0.60 | 0.75 |
| Support Vector Machine | 0.64 | 0.63 | 0.64 | 0.64 | 0.82 |
| Multilayer Perceptron | 0.66 | 0.67 | 0.67 | ||
| Random Forest | 0.64 | 0.62 | 0.64 | 0.62 | 0.81 |
| XGBoost | 0.66 | 0.63 | 0.66 | 0.64 | 0.82 |