Why Applied Machine Learning Is Hard
How to Handle the Intractability of Applied Machine Learning.
Applied machine learning is challenging.
You must make many decisions where there is no known “right answer” for your specific problem, such as:
- What framing of the problem to use?
- What input and output data to use?
- What learning algorithm to use?
- What algorithm configuration to use?
This is challenging for beginners that expect that you can calculate or be told what data to use or how to best configure an algorithm.
In this post, you will discover the intractable nature of designing learning systems and how to deal with it.
After reading this post, you will know:
- How to develop a clear definition of your learning problem for yourself and others.
- The 4 decision points you must consider when designing a learning system for your problem.
- The 3 strategies that you can use to specifically address the intractable problem of designing learning systems in practice.
Let’s get started.
Overview
This post is divided into 6 sections inspired by chapter 1 of Tom Mitchell’s excellent 1997 book Machine Learning; they are:
- Well-Posed Learning Problems
- Choose the Training Data
- Choose the Target Function
- Choose a Representation of the Target
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