Abstract

The goal of this research is to develop a model evaluation program using confusion matrix in Python. Model evaluation is used to measure the performance of the applied model by comparing the predicted data with the actual data. Confusion matrix is used to summarize the predictions of the applied model and compute the evaluation metrics. The basic steps of model evaluation using confusion matrix are explained: preparing data (actual and predicted), computing confusion matrix, computing totals (sum of items, diagonal, rows, and columns), computing evaluation metrics (accuracy, precision, recall, and F1-score), printing evaluation metrics, and plotting confusion matrix. The developed program was tested on an experimental dataset. The program successfully performed the basic steps of model evaluation using confusion matrix and provided the required results.

References

• Sammut, C., & Webb, G. I. (2011). "Encyclopedia of Machine Learning". Springer Science & Business Media.
• Jung, A. (2022). "Machine Learning: The Basics". Singapore: Springer.
• Kubat, M. (2021). "An Introduction to Machine Learning". Cham, Switzerland: Springer.
• Li, H. (2023). "Machine Learning Methods". Springer Nature.
• Dey, A. (2016). "Machine Learning Algorithms: A Review". International Journal of Computer Science and Information Technologies, 7 (3), 1174-1179.
• Bonaccorso, G. (2018). "Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning". Packt Publishing.
• Jo, T. (2021). "Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning". Springer.
• Jordan, M. I., & Mitchell, T. M. (2015). "Machine Learning: Trends, Perspectives, and Prospects". Science, 349(6245), 255-260.
• Forsyth, D. (2019). "Applied Machine Learning". Cham, Switzerland: Springer.
• Chopra, D., & Khurana, R. (2023). "Introduction to Machine Learning with Python". Bentham Science Publishers.
• Müller, A. C., & Guido, S. (2016). "Introduction to Machine Learning with Python: A Guide for Data Scientists". O'Reilly Media.
• Zollanvari, A. (2023). "Machine Learning with Python: Theory and Implementation". Springer Nature.
• Raschka, S. (2015). "Python Machine Learning". Packt Publishing.
• Sarkar, D., Bali, R., & Sharma, T. (2018). "Practical Machine Learning with Python". Apress.
• Swamynathan, M. (2019). "Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics using Python". Apress.
• Kong, Q., Siauw, T., & Bayen, A. (2020). "Python Programming and Numerical Methods: A Guide for Engineers and Scientists". Academic Press.
• Yale, K., Nisbet, R., & Miner, G. D. (2017). "Handbook of Statistical Analysis and Data Mining Applications". Elsevier.
• Unpingco, J. (2022). "Python for Probability, Statistics, and Machine Learning". Cham, Switzerland: Springer.
• Brandt, S. (2014). "Data Analysis: Statistical and Computational Methods for Scientists and Engineers". Springer.
• VanderPlas, J. (2017). "Python Data Science Handbook: Essential Tools for Working with Data". O'Reilly Media.
• James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). "An Introduction to Statistical Learning: With Applications in Python". Springer Nature.
• Aggarwal, C. C. (2020). "Data Classification: Algorithms and Applications". CRC Press
• Novaković, J. D., Veljović, A., Ilić, S. S., Papić, Ž., & Tomović, M. (2017). "Evaluation of Classification Models in Machine Learning". Theory and Applications of Mathematics & Computer Science, 7(1), 39.
• Zheng, A. (2015). "Evaluating Machine Learning Models: A Beginner's Guide to Key Concepts and Pitfalls". O'Reilly Media.
• Raschka, S. (2018). "Model Evaluation, Model Selection, and Algorithm Selection in Machine Larning". arXiv preprint arXiv:1811.12808.
• Rainio, O., Teuho, J. & Klén, R. (2024). "Evaluation Metrics and Statistical Tests for Machine Learning". Scientific Reports, 14 (1), 6086.
• Naidu, G., Zuva, T., & Sibanda, E. M. (2023). "A Review of Evaluation Metrics in Machine Learning Algorithms". In Computer Science On-line Conference (pp. 15-25). Cham: Springer International Publishing.
• Hossin, M., & Sulaiman, M.N, (2015). "A Review on Evaluation Metrics for Data Classification Evaluations". International Journal of Data Mining & Knowledge Management Process, 5, 01-11.
• Python: https://www.python.org
• Numpy: https://www.numpy.org
• Pandas: https:// pandas.pydata.org
• Matplotlib: https://www. matplotlib.org
• NLTK: https://www.nltk.org
• SciPy: https://scipy.org
• SK Learn: https://scikit-learn.org
• Kaggle: https://www.kaggle.com