Since this series is about Machine learning, I am sure when you complete the series, you will start looking at every problem through the lens of Machine Learning. This is what many companies or tech teams are doing. They are applying Machine learning for the sake of it. Some wise person rightly said, “When you have a hammer in your hand everything around you will appear as a nail”.

It is worth reminding us of the golden rule of Machine Learning.

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Remember, the purpose of Machine learning, or Deep Learning, or any other tool is to solve a user problem. The tool is not the end but a means to get there.

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Now, let us try to understand when do we apply Rule Based vs Machine Learning vs Deep Learning.

There are four important components in the Artificial Intelligence Problem statement - Input, Output, Features, and Possible Values of the Feature.The purpose of Artificial intelligence in simple terms is finding a function F(x) that allows the Machine to map input to Output. Let us take an example. Suppose, given the address of the house, I want to find at what price the house will sell.

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In this case, Input is the house identifier say the address of the house. The output is the price at which the house will sell. The Features are #BR, #BA, Sq Ft, etc. and the values of the features are say for #BR - 2 BR, 3 BR, 4 BR, etc.

Now imagine a scenario where there are only four configurations of houses

This is where the Rule-Based Model shines. One can easily convert this into a series of If-Else conditions to create a rule such that if #BR is 2 and # BA is 2 then the price is $0.8M. In this case, based on Features and Values of the Features from historical data, it is Human (A programmer) who creates the rule to map Input to Output.

However, we all know, that in the real world, there are many more features than just these two, and possible values that some of the features, such as Sq ft, can take are also not finite. In that case, for a programmer to create a rule or set of rules will be impossible or even if written will require millions of ‘If Else’ conditions, which is not feasible to manage. This renders the Rule-Based Model ineffective for many situations.In comes Machine Learning. In Machine learning, while Humans (A data-scientist) will identify which features are important, it is machines that will learn the relationship between features and possible values to create a rule or set of rules that map Input to Output.

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In summary:

In Rule-Based Models, based on Features and Values of the Features from historical data, it is the Human (The programmer) who creates the rule to map Input to Output.
In Machine Learning Models, while the data scientists will identify which features are important, it is the Machine that will learn based on data the Rules that map Input to Output.
In Deep Learning Models, Machines will extract what features are important, and also create a rule that maps Input to Output.

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Now suppose, we have a scenario where we have three Models that generate three functions as shown below

Case A).     F1: Ax + K                                      ⇒ A rule-based Model

Case B)       F2: Ax + By + cZ^2 + … + K  ⇒ A Machine Learning Model

Case C)       F3: ?                                                 ⇒ A black box model that maps input to output but we do not know what that model looks like. This is the problem of interpretability that we will discuss later in Module 3.

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If we assume everything else is constant i.e. the models take the same compute, training time, and have the same accuracy of prediction. Which one shall we choose?

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I hope you answered Case A) F1. Why? Because if a model is simple and achieves the same result so why introduce more complexity? But in case you chose B or C. Let me explain why that would not be the smartest choice in this scenario.

Many tech people, fall prey to choosing a model that is more advanced or complex. Why? Because it looks great on slide decks. Also, many times company culture rewards complexity. You are talked of as a genius who has implemented the most advanced model. You become a folklore. you get promoted. This is great as long as the model works. But then there is a time when the business context changes. The model is not performing well. Revenue is dropping. Your manager is breathing down your neck, asking why the model is not working. You respond - I do not know. This is a black box model. We will have to retrain the model. Now guess, how many managers like hearing ‘I do not know’. No one. Now of course this is a hypothetical scenario and stretched a bit but you will be surprised to know how many times the Tech team falls prey to this syndrome.

Whenever you are leading or interacting with a tech team and need to make a decision, remind them that simplicity is often the better choice.

This will benefit you in the long run.