
Are you tired of relying solely on accuracy to evaluate your classification models? Do cryptic metrics like F1 score or Matthew's Correlation Coefficient leave you scratching your head?
Join us as we unlock the secrets of binary classification performance measurement and go beyond simple accuracy. We'll explore a comprehensive set of 65 metrics, each providing unique insights into your model's strengths and weaknesses.
We'll break down complex concepts into easily understandable terms and discuss how these metrics can help you make more informed decisions about your models. We'll also unveil TasKar, a powerful new dashboard that visualizes your classification results with innovative graphics, making it easier than ever to interpret your model's performance.
Whether you're a seasoned machine learning expert or just starting, this podcast will equip you with the knowledge and tools to evaluate and compare your binary classification models confidently.
Tune in to discover the full potential of your classification models!
Download TasKar for free: https://github.com/gurol/TasKar (Best viewed with free Libre Office or Apache Open Office.
👉 Please cite my article as follows: Canbek, G., Taskaya Temizel, T., & Sagiroglu, S. (2021). TasKar: A research and education tool for calculation and representation of binary classification performance instruments. IEEE 14th International Conference on Information Security and Cryptology (ISCTurkey), 105–110. https://doi.org/10.1109/ISCTURKEY53027.2021.9654359