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Friday, August 04, 2017

Running Analytics Experiments Systematically

Really growing to like Jason Brownlee's Machine Learning Mastery posts.  He is starting to seem like a trusted adviser.   Really useful stuff, the most recent example talks about the process of getting machine learning process done.   Even if you don't use all of these ideas, many can really help.   At least until it is all automated.  This should be taught, it is not.  Practical stuff.  Follow him, buy his for-pay stuff.  Thanks Jason.

How to Plan and Run Machine Learning Experiments Systematically
by Jason Brownlee on August 4, 2017 in Machine Learning Process

Machine learning experiments can take a long time. Hours, days, and even weeks in some cases.

This gives you a lot of time to think and plan for additional experiments to perform.

In addition, the average applied machine learning project may require tens to hundreds of discrete experiments in order to find a data preparation model and model configuration that gives good or great performance.

The drawn-out nature of the experiments means that you need to carefully plan and manage the order and type of experiments that you run.

You need to be systematic.
In this post, you will discover a simple approach to plan and manage your machine learning experiments.

With this approach, you will be able to:

-Stay on top of the most important questions and findings in your project.
-Keep track of what experiments you have completed and would like to run.
-Zoom in on the data preparations, models, and model configurations that give the best performance.

Let’s dive in.   .... "       (The rest is at the link above) 

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