“Managing the uncertainty that is inherent in machine learning for predictive modeling can be achieved via the tools and techniques from probability, a field specifically designed to handle uncertainty,” writes Jason Brownlee in A Gentle Introduction to Uncertainty in Machine Learning. In his post, one will learn:
- Uncertainty is the biggest source of difficulty for beginners in machine learning, especially developers.
- Noise in data, incomplete coverage of the domain, and imperfect models provide the three main sources of uncertainty in machine learning.
- Probability provides the foundation and tools for quantifying, handling, and harnessing uncertainty in applied machine learning.