5 Explainable Machine Learning Models You Should Understand

Why use complex model when simple do trick?

Adam Shafi
Towards Data Science
11 min readJun 16, 2021

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Photo by Nick Morrison on Unsplash

Introduction

As we know, Machine Learning is ubiquitous in our day to day lives. From product recommendations on Amazon, targeted advertising, and suggestions of what to watch, to funny Instagram filters.

If something goes wrong with these, it probably won’t ruin your life. Maybe you won’t get that perfect selfie, or maybe companies will have to spend more on advertising.

How about facial recognition in law enforcement? Loan or mortgage applications? Driverless vehicles?

In these high-risk applications, we can’t go in blind. We need to be able to dissect our model, we will need to be able to understand and explain our model before it goes anywhere near a production system.

Explainable Machine Learning is essential when we are making decisions about people that can negatively impact their lives such as mortgages or credit scoring.

Using explainable models also allows more efficient debugging as well as better understand of fairness, privacy, causality, and more trust in the model.

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