Applied Machine Learning Process

Last Updated on July 5, 2019

The Systematic Process For Working Through Predictive Modeling Problems
Delivers Above Average Results

Over time, working on applied machine learning problems you develop a pattern or process for quickly getting to good robust results.

Once developed, you can use this process again and again on project after project. The more robust and developed your process, the faster you can get to reliable results.

In this post, I want to share with you the skeleton of my process for working a machine learning problem.

You can use this as a starting point or template on your next project.

5-Step Systematic Process

I liked to use a 5-step process:

  1. Define the Problem
  2. Prepare Data
  3. Spot Check Algorithms
  4. Improve Results
  5. Present Results

There is a lot of flexibility in this process. For example, the “prepare data” step is typically broken down into analyze data (summarize and graph) and prepare data (prepare samples for experiments). The “Spot Checks” step may involve multiple formal experiments.

It’s a great big production line that I try to move through in a linear manner. The great thing in using automated tools is that you can go back a few
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