Perhaps the most surprising question to ask, is whether you need machine learning to solve your problem. There is after all, a good reason why this section is the fourth in the chapter—we must understand what exactly is a problem is; and understand what an algorithm is before we can raise the question: do you need machine learning?
The first question to ask is of course: do you have a problem you need to solve? I assume the answer is yes, because we live in the world and are part of the world. Even ascetics have problems they need solved. But perhaps the question should be more specific: do you have a problem that can be solved with machine learning?
I've consulted a fair bit, and in my early days of consulting, I'd eagerly say yes to most enquiries. Ah, the things one does when one is young and inexperienced. The problems would often show up after I said yes. It turns out many of these consulting enquiries would be better served by having a more thorough understanding of the business domain and a more thorough understanding of computer science in general.
A common variant of a problem that is brought to me often requires information retrieval solutions, not machine learning solutions. Consider the following request I received several years ago:
I am XXXX. We met at YYYY meetup a few months ago. My company is currently building a machine learning system that extracts relationships between entities. Wondering if you may be interested to catch up for coffee?
Naturally, this piqued my interest—relationship extraction is a particularly challenging task in machine learning. I was young, and ever so eager to get my hands on tough problems. So I sat down with the company, and we worked out what was needed based on surface information. I suggested several models, all of which were greeted with enthusiasm. We finally settled on an SVM-based model. Then I got to work.
The first step in any machine learning project is to collect data. So I did. Much to my surprise, the data was already neatly classified, and entities already identified. Further, the entities have a static, unchanging relationship. One type of entity would have a permanent relationship with another type of entity. What was the machine learning problem?
I brought this up after one and a half month's worth of data gathering. What was going on? We have clean data, we have clean relationships. All new data had clean relationships. Where is the need for machine learning?
It later emerged that the data came from manual data input, which was at the time required by law. The entity relationships were defined fairly strictly. The only data requirement they really needed was a cleaned up database entity-relationship diagram. Because their database structure was so convoluted, they could not really see that all they needed to do was to define a foreign-key relationship to enforce the relationship. When I had requested the data, the data had came from individual SQL queries. There was no need for machine learning!
To their DBA's credit, that was what their DBA had been saying all along.
This taught me a lesson: Always find out if someone really needs machine learning solutions before spending time working on it.
I've since settled on a very easy way of determining if someone needs machine learning. These are my rules of thumb
- Can the problem in this form: "Given X, I want to predict Y"
- A what-question is generally suspect. A what question looks like this: "I want to know what is our conversion rate for XYZ product"
