What is Overfitting?
It’s time to tell you about one of the biggest problems in machine learning — something that might infuriate you as a future data scientist. It’s called overfitting, and it impacts how algorithms perform on the data you need for analysis. We’ll answer the question, “What is overfitting?” below and explain how you can become a better data scientist on one of The Data Incubator’s renowned programs.
Definition of Overfitting in Machine Learning
Overfitting is a type of machine learning behavior that can happen when you least expect it. It’s when a machine learning model works perfectly on training data but fails to make accurate predictions for new unseen data.
So why is this a problem?
As a data scientist, you might use a particular machine learning model to predict future outcomes for the company you work for. You’ll train that model with an existing data set before using it on new data. If that model struggles to make predictions for unseen data, you can’t perform analytical or classification tasks effectively. Your data analysis will have bias, rendering it practically useless.
The problem of overfitting dates back to at least the 1970s and became even more of an issue during the development of neural networks in the 1990s. Today, some data scientists consider overfitting “the biggest obstacle in machine learning.”
Now you know the answer to the question, “What is overfitting?” learn how to avoid it on one The Data Incubator’s programs. Our Data Science Bootcamp, for example, provides all the skills you need to become a more proficient data scientist.
How Does Overfitting Happen?
Overfitting often occurs when a data scientist overtrains a machine learning model with training data. That causes the model to memorize the training data instead of generalizing itself to brand new data. You can also experience overfitting if the size of your training data is too small. A machine learning model might identify patterns that don’t exist in this data and be unable to make predictions from new data.
These are big problems, so you need to learn techniques that prevent overfitting from happening in the first place. For example, testing the performance of your machine learning model before using it in the real world. Or adding more training data to your sample.
You’ll learn more about machine learning models in Data Science Essentials, which serves as a primer to data science. It can take you as little as eight weeks part-time to finish this program!
What is Overfitting? The Biggest Problems You’ll Experience
There are no benefits of overfitting, just lots of negatives. Here are some of the ways overfitting makes your life difficult as a data scientist:
- Wasted time: If a machine learning model can’t generalize to unseen data, there’s not a lot you can do about it. You’ll probably have to have to ditch that model and create a new one from scratch, which will delay analysis.
- Wasted money: Time is money, and creating a new machine learning model will likely eat into your company’s budget. It all depends on how complex it is, but companies can spend thousands of dollars developing a model.
- Inaccurate analysis: If you don’t know overfitting has occurred, you might presume your model is generating accurate predictions from data sets. However, your data will be flawed and contain inaccuracies and inconsistencies. That’s why it’s important to spot the signs of overfitting, such as identifying training data with a lower error rate and high variance.
What are you waiting for?
Want to take a deep dive into the data science skills you need to become a successful data scientist? The Data Incubator has got you covered with our immersive data science bootcamp.
Here are some of the programs we offer to help you turn your dreams into reality:
- Data Science Bootcamp: This program provides you with an immersive, hands-on experience. It helps you learn in-demand skills so you can start your career in data science.
- Data Engineering Bootcamp: This program helps you master the skills necessary to effortlessly maintain data, design better data models, and create data infrastructures.
We’re always here to guide you through your journey in data science. If you have any questions about the application process, consider contacting our admissions team.