10 Examples of How to Use Statistical Methods in a Machine Learning Project

Last Updated on August 8, 2019

Statistics and machine learning are two very closely related fields.

In fact, the line between the two can be very fuzzy at times. Nevertheless, there are methods that clearly belong to the field of statistics that are not only useful, but invaluable when working on a machine learning project.

It would be fair to say that statistical methods are required to effectively work through a machine learning predictive modeling project.

In this post, you will discover specific examples of statistical methods that are useful and required at key steps in a predictive modeling problem.

After completing this post, you will know:

• Exploratory data analysis, data summarization, and data visualizations can be used to help frame your predictive modeling problem and better understand the data.
• That statistical methods can be used to clean and prepare data ready for modeling.
• That statistical hypothesis tests and estimation statistics can aid in model selection and in presenting the skill and predictions from final models.

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

Let’s get started.