Overfitting and underfitting in machine learning: Causes, indicators, and how to fix them

A machine learning model is only considered good when it can make accurate predictions on new information (unseen data). It might sound simple enough, but the tricky part is finding the sweet spot between learning too much and too little.

The balance isn’t easy to get, leading to two big issues—overfitting and underfitting. Both can mess with your model’s performance, making it less reliable and not so great at predictions. If your model isn’t balanced, you get problems like lower accuracy and poor generalization.

Pictorial explanation of the tradeoff between underfitting and overfitting. Source: Paper

Getting the right balance is how you build models that are not only accurate but also dependable in real-world scenarios. In this article, we’ll break down overfitting and underfitting, what causes them, how to spot them, and, most importantly, how to fix them.

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Let’s begin by defining overfitting and underfitting.

Table of Contents

• What is Overfitting?
• What is Underfitting?
• Causes of overfitting and underfitting
• Indicators of overfitting and underfitting
• How to address overfitting
• How to address underfitting
• Conclusion

What is overfitting?

Imagine you're teaching a child to recognize cats. You show them pictures of fluffy Persians, sleek Siamese, and playful tabbies. They quickly learn to identify these types of cats with pointy ears and whiskers. But then, you show them to a hairless Sphynx cat. Confused, they say it's not a cat!

This is similar to what happens in overfitting. The machine learning model becomes too focused on the specific data it was trained on (the fluffy, sleek, and playful cats). It essentially memorizes the training data, including all its quirks and nuances, rather than learning the underlying patterns of what makes a cat a cat.

In more technical terms, overfitting happens when a model learns the training data too well, capturing even the noise and random fluctuations within it. When this happens, the model will perform exceptionally well on the training data but fail to generalize to new, unseen data. It's like a student who aces the practice test but bombs the real exam.

Key characteristics of overfitting:

• Too complex: It uses way too many parts (parameters and features) for a simple task. It works perfectly for your exact setup, but if anything changes even a tiny bit, the whole thing breaks down. It's like building a super complicated Rube Goldberg machine just to turn on a light switch. That's basically what an overfit model does.
• Poor generalization: The model struggles to accurately predict outcomes for new data. Remember our cat-loving kid who got confused by the Sphynx? Overfit models are the same way. They get so good at recognizing the exact things they've seen before that they freak out when they see something new.
• Low training error, high test error: Overfit models typically have very low error rates on the training data but significantly higher ones on a separate test dataset, much like the student who gets 100% on all the practice tests because they memorized the answers, but then completely bombs the actual exam because the questions are different.

Think of it like this for more complex problems: Imagine trying to connect all the dots on a graph with a single line. You could do it, but you'd end up with a crazy, squiggly line that goes all over the place. That's overfitting! It matches the existing dots perfectly, but it's so focused on those specific dots that it misses the bigger picture and won't be able to connect any new dots you add.

In the next section, we'll explore the opposite problem: underfitting.

What is underfitting?

Alright, so we've seen what happens when a model goes overboard and tries to learn everything, even the random noise. But what about the opposite problem? What if it doesn't learn enough? That's what underfitting is.

Imagine you're trying to teach someone to ride a bike, but you only show them how to balance on a stationary bike. Sure, they might get the hang of staying upright, but when they try to ride a real bike, they'll fall flat on their face because they haven't learned the essential skill of pedaling and steering.

Overfitted models can fit to noise in the training data, while underfitted models miss a lot of the detail. Source: Paper

That's underfitting in a nutshell. The model is too simple to capture the underlying patterns in the data. It's like trying to draw a straight line through a set of points that clearly form a curve. No matter how hard you try, that line just won't fit.

Here are the characteristics of underfitting:

• Too Simple: Underfit models are usually too simple. They are like a basic bike with no gears, no brakes, and definitely no cool gadgets. They need to be equipped to handle the complexity of the real world (or the data).
• Missing the point: Underfit models fail to grasp the relationships between features and target variables. It's like trying to summarize a whole book with a single sentence. You'll miss all the important details and nuances.
• Bad all around: Unlike overfitting, where the model does well on the training data but poorly on new data, underfitting leads to poor performance on both. It's like failing both the practice test and the real exam.

Using an underfit model is like using a hammer to try and fix a computer. You need the right tools for the job! If your model is too simple, it won't be able to learn the complexities of the data, leading to poor predictions and unreliable results.

Causes of overfitting and underfitting

Okay, so we've got these two issues, overfitting and underfitting. Let's break down some of their causes:

Overfitting

• Too Much Complexity: If your model has too many parameters for the amount of data, it'll get lost in the details and overfit.
• Not enough data: Another cause of overfitting is when your model doesn't have enough data to learn from; it'll overfit to the limited examples it has. It's like trying to learn a language from a single page of a dictionary. You might memorize those words, but you won't be able to hold a conversation.
• Noisy data: Lastly, when you have a dataset with errors or irrelevant information, your model might learn those mistakes, leading to overfitting.

Underfitting

• Too much simplicity: When you build a simple model, like a basic feed-forward network, and train it on a small dataset, it won't be able to capture the complexity of the data. The model will underfit, causing it to perform poorly.

The tradeoff between overfitting and underfitting. Source: Towards data science

• Not enough training: Not training your model for long enough will cause underfitting as it won't have time to learn the patterns in the data. It's like expecting someone to run a marathon after only jogging for five minutes. They’ll need more practice.

Overfitting is often caused by complexity and noise, while underfitting stems from simplicity and lack of training. Finding the right balance is key to building an accurate and reliable model.

Indicators of overfitting and underfitting

There are some telltale signs to watch out for to know if your model is suffering from overfitting or underfitting. Here are some of them:

Overfitting

• Stellar performance on training data but fails the test: This is the classic sign of overfitting. Your model gets an A+ on the homework (training data) but fails miserably on the exam (test data). It's just memorizing the answers instead of understanding the concepts.
• Huge gap between training and test error: If your training error is super low (close to zero) but your test error is way high, you've got a case of overfitting on your hands. It's like bragging about your amazing basketball skills after practicing alone in your backyard but then tripping over your own feet in a real game.
• Overly sensitive to change: Overfit models often have a ton of parameters and features, making them overly complex and sensitive to any tiny change in the data.

Underfitting

• Bad performance all around: Unlike overfitting, where the model at least does well on the training data, underfitting leads to poor performance on both the training and test data. It's like showing up unprepared for both the practice test and the real exam.
• High error rates: Both your training and test errors will be high, indicating that the model isn't capturing the underlying patterns in the data.

Overfitting is like a student who overstudies and gets anxious during the real exam, while underfitting is like a student who doesn't study at all and just wings it. The key is to find the sweet spot in the middle – a model that's prepared but not overly stressed.

How to address overfitting

When you have a model that overreacts to every little detail in the training data, we can calm things down with a few strategies:

• Simplify: Sometimes, you just need to step back and simplify things. Try reducing the number of features or parameters in your model. Think of it like decluttering your house – you get rid of the stuff you don't need and focus on the essentials.
• Get more data: The more data your model has to learn from, the less likely it is to get fixated on the quirks of any particular data point. It's like expanding your vocabulary – the more words you know, the better you can understand and express yourself.
• Data cleaning: Make sure your data is free of errors, inconsistencies, and irrelevant information. Just like you wouldn't want to learn a song from a noisy recording, your model needs clean data to learn effectively.
• Cross-validation: You can use cross-validation by splitting your data into multiple folds and training your model on different combinations. It's like taking multiple practice tests to prepare for the real exam – it gives you a better idea of how your model will perform on unseen data.

When using cross-validation, it’s important to carry out feature selection independently for each iteration. Source: Paper

• Regularization: Regularization is a fancy term for adding constraints to your model to prevent it from getting too excited about the training data. It's like setting boundaries for a child – it helps them learn self-control and avoid getting carried away.

How to address underfitting

If your model is a bit of a slacker and is not putting in the effort to learn the patterns in the data, here's how to give it a nudge:

• Increase model complexity: If your model is too simple, it might be time to upgrade it. Try adding more features, parameters, or layers to your model. It's like giving your bike some gears and brakes – it can handle more complex terrain.
• Train longer: Just like a marathon runner needs to train for a long time, your model might need more time to learn the data. Increase the number of training epochs or iterations to allow it to absorb the information.
• Feature engineering: You can also try creating new features from your existing data to help your model capture the underlying patterns. It's like giving your model glasses – it enhances their sight and makes it more accurate.
• Reduce regularization: If you've been a bit too strict with your model, it might be time to loosen the reins a bit. Reducing regularization can give your model more freedom to learn from the data.
• Introduce dropouts: With this technique, you randomly drop out some neurons during training, which forces the network to learn more robust features and prevents over-reliance on any single neuron. Think of it like a team where everyone needs to pull their weight. Dropout is like randomly giving some team members a day off during training, forcing the rest of the team to step up and learn the problem better on their own. That way, no one gets lazy or relies too much on others, and the whole team becomes stronger and more independent.

Early dropout improves accuracy when the number of learning rate warmup epochs varies. Source: Paper

Remember, the goal is to find the Goldilocks zone – a model that's neither too complex nor too simple. It's all about balance. By carefully monitoring your model's performance and adjusting your strategies, you can create a machine-learning model that's accurate, reliable, and ready to tackle real-world challenges.

While all of these are good so far, one more thing to do that will help your model learn better is finetuning your hyperparameters. Hyperparameters are the external settings that control your model. They're like the knobs and dials on a machine – adjusting them can significantly impact your model's performance.

Some examples of hyperparameters include:

Learning rate: How quickly your model learns from the data.

Number of hidden layers: The complexity of your neural network.

Batch size: How much data your model processes at a time.

Regularization strength: How much you constrain your model to prevent overfitting.

There are several techniques for fine-tuning hyperparameters, but we’ll only talk about a few popular ones. The first is manually adjusting hyperparameters and observing the effect on your model's performance.

Another option is systematically trying out different combinations of hyperparameters within a predefined range (grid search). You can also use random search, which is when you randomly sample hyperparameter values from a predefined distribution. It's like throwing darts at a dartboard and seeing which combination of values gives you the best score.

Finally, you can use Bayesian optimization, which is using a probabilistic model to guide the search for optimal hyperparameters.

Fine-tuning hyperparameters is an iterative process. You'll need to experiment, analyze the results, and make adjustments until you find the best combination for your specific model and dataset.

Conclusion

And there you have it! We've journeyed through the world of overfitting and underfitting, those pesky challenges that can trip up even the most seasoned machine-learning enthusiast.

Remember that finding the sweet spot between these two extremes is like walking a tightrope. It requires careful balancing, constant monitoring, and a willingness to adjust your approach along the way.

By understanding the causes, recognizing the signs, and mastering the techniques to address these issues, you'll be well-equipped to build machine learning models that are not only accurate but also reliable, robust, and ready to tackle real-world problems.

If you need a helping hand with the computational heavy lifting, remember that CUDO Compute is here to provide the scalable infrastructure you need. Happy modeling!