A Gentle Introduction to Uncertainty in Machine Learning

Last Updated on September 25, 2019

Applied machine learning requires managing uncertainty.

There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of data collected from the domain, and in the imperfect nature of any models developed from such data.

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.

In this post, you will discover the challenge of uncertainty in machine learning.

After reading this post, you will know:

• 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.

Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

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