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4 Common Pitfalls When Building Machine Learning Model

Last Updated on July 3, 2022 by Editorial Team

Author(s): Gencay I.

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

Most Common Issues When Building a Machine Learningย Model

Photo by charlesdeluvio onย Unsplash
Contents
ยท Introduction
โˆ˜ 1. Is your source up to date?
โˆ˜ Solution
โˆ˜ 2. Not Enough Data?
โˆ˜ Solution
โˆ˜ 3. Is Aristotle right about quality?
โˆ˜ Solution
โˆ˜ 4. Can your model be perfect?
โˆ˜ Solution
ยท Conclusion

Introduction

When building a Machine Learning Model for your company, for your portfolio, or for fun, there are some steps to take. And there are some other things you should avoid to increase your model accuracy. In this article, I try to warn you about 4 Common Pitfalls, when building a machine learning model. Although tons of cautions, you should take, while applying Machine Learning Model, when you avoid doing these steps, your model will beย okay.

1. Is your source up toย date?

Photo by Markus Winkler onย Unsplash

These days, when building machine learning, it is common to find sourcesย online.

Like in GitHub pages or course materials.

Generally, that is a self-evolving process but sometimes you have to be careful aboutย that.

Sometimes, online documentation can be updated according to the version changes, but if you looked at the codes from outdated documents or Github pages, that could result in you doing the debugging process.

That means, the function name or arguments might change, sometimes even the name of the function can beย changed.

Solution

To avoid these problems, I always look at the date of the source I amย reading.

After that, check the library version in that article and compare it with the currentย one.

For example, if you are coding in Python and you are using Scikit Learn, it would be good to check the Scikit-learn release historyย here.

2. Not Enoughย Data?

Photo by Markus Spiske onย Unsplash

Does More Data Alwaysย Better?

Simple but important.

Most of the time more data would beย better.

It is not the only way of improving your Machine Learning modelโ€™s performance.

Solution

On the other hand, research shows, that more data would be better most of theย time.

Sometimes, adding more data may increase the cost, on the other hand, it would increase your model's performance too as shown in Andrew Ngโ€™s graphย below;

Reference: Deep Learningย AI

3. Is Aristotle right aboutย quality?

Photo by Alex Shute onย Unsplash

โ€œQuality is not an act it is aย habit.โ€

Aristotle

Actually, I assume if Aristotle would live in that era, he will be a great Data Scientist.

Data Quality is very important when you build a Machine Learningย model.

Especially, if your model is used in production.

However, sometimes, things can go out of yourย control.

Especially when your model is in production.

Solution

If you want to build that habit, you should check your incoming data frequently to avoid bad incomingย data.

Moreover, setting limits to your incoming data would prevent bad incoming data and that will guard your model efficiency.

4. Can your model beย perfect?

Photo by Vitolda Klein onย Unsplash

Letโ€™s be straightforward, no.

Chasing perfection can be a great thing but not in Machine Learning.

In machine learning it might be a motivation killer for you and also would cause overfitting.

What does overfitting mean?

Overfitting means simply your model is way perfect, so it is too good to beย true.

โ€œPerfection is the enemy of progress.โ€

Winston Churchill

Solution

To avoid overfitting, one thing to do is to simplify yourย model.

By dimension reduction or feature engineering, you can easily be simplifying your model and overcome overfitting.

Conclusion

Photo by Robynne Hu onย Unsplash

The era we are in is really interesting.

Daily improvements change the way of living of humanity.

Machine Learning and its applications are really important in thatย aspect.

Let's look at the Real-life machine learning applications quickly;

  • Face Recognition- These days even our mobile phones use thisย tech.
  • Speech Recognition- Siri is a famousย example.
  • Recommender System- Mostly known on Netflix, you may like this filmย too.

And machine Learning comes into almost every cutting-edge businessย space;

  • Healthcare– Detecting Tumors
  • Finance– Predicting Stockย Prices
  • Law– Consumerย Analysis

And manyย more.

So independent from your business space, in my opinion, either you across with Machine Learning or you will be in the near future and one stepย forward;

โ€œMachine learning is the last invention that humanity will ever need to make.โ€ Nickย Bostrom

Thanks for reading myย article.


4 Common Pitfalls When Building Machine Learning Model was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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