Overfitting and Underfitting in Machine Learning

When you decide to build machine learning models, you will likely come across challenges where your model might fail to perform. One of the initial challenges can occur during the training phase on your dataset. This is where the concepts of overfitting and underfitting become important for your understanding. These issues should be understood to come up with solid and transferable models that can push practical applications. Through advanced examples and explanations, you will learn about the causes, differences, and solutions to overfitting and underfitting. Additionally, you will gain practical code examples to enhance your coding skills. Whether you’re a novice or an aspiring data scientist, the blog will assist you in creating smarter models with better predictive abilities.

Table of Contents:

• Understanding Overfitting in Machine Learning
• Top Causes of Overfitting in Machine Learning Models
• Proven Techniques to Prevent and Fix Overfitting
• What is Underfitting in Machine Learning
• Common Reasons Behind Underfitting in ML Algorithms
• Proven Techniques to Prevent and Fix Underfitting
• Bias-Variance Tradeoff
• Overfitting vs. Underfitting
• Best Practices for Model Evaluation and Generalization in ML
• Conclusion

Understanding Overfitting in Machine Learning

Imagine your model as a student who is trained to take an exam by memorizing answers, instead of understanding the concepts. They have a chance of passing the exam if the examination paper has a known pattern. However, the student will fail in case of a different pattern of questions. Likewise, in an overfitted model, the model memorizes the training set rather than learning general patterns.

A machine learning model can overfit by learning not just the meaningful patterns (signal) but also the irrelevant variations, noise, and outliers in the training data. This results in a model that performs exceptionally well on the training set but fails to generalize to new or unseen data. It may be possible that, based on your training performance, you believe that you have an accurate model, but when put to the test, the performance of your model declines, a clear case of overfitting.

To understand what overfitting is, we can use a simple example of polynomial regression:

Code:

Output:

Explanation: Here, this code will create a nonlinear dataset and a high-order model, a polynomial one. You will see in the output that although the red curve fits the data points very well, it zigzags unpredictably and fails to capture a general trend, which means that the model is fitting some noise instead of showing a typical trend. The result is inaccurate output in predicting new values, and this is the meaning of overfitting.

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Top Causes of Overfitting in Machine Learning Models

Overfitting usually occurs due to specific modeling or data issues. It is often caused by certain modelling or data problems that encourage the algorithm to overlearn the training set. The root causes will assist you in taking precautionary measures before it happens.

• Lack of training data is one of the major causes. When you have insufficient data, the model attempts to explain insufficient examples, and in the process, instead of generalization, memorization occurs. Even an effective algorithm, such as a decision tree or deep neural network, may overfit on such sparse data.
• The other reason is model complexity. Highly flexible models can include complicated models such as high-degree polynomial regressors or a deep neural network, with excessive parameters. This flexibility enables them to overfit the training set to a great extent, but yields poor generalization. The more careful you need to be to avoid overfitting.
• Training for too many epochs with iterative algorithms, like gradient descent, also causes overfitting. The longer the model trains, the more it will attempt to minimize error on training data, and it will begin learning noise, particularly in the absence of an early stopping routine. Gradient boosting can help, but it cannot guarantee a reduction in overfitting.
• Another factor that can cause overfitting is noisy data. The presence of outliers or errors in the data confuses the model and makes it learn nothing worthwhile. Without cleaning the data correctly, the model will regard this noise as a valid structure and apply it to its reasoning.
• Finally, a lack of regularization methods, such as L1 (Lasso) or L2 (Ridge), removes constraints on model parameters. The absence of regularization gives.

The causes presented here can be recognizable and comprehended, and through them, more satisfactory models can be crafted that are capable of avoiding overfitting. In a further section, we will discuss better methods to avoid and eliminate overfitting.

Proven Techniques to Prevent and Fix Overfitting

Overfitting may greatly restrict the power of your machine learning models. Luckily, there exists a variety of methods to minimize or remove it, therefore, making your model generalize to new data.

• Cross-validation is one of the most popular techniques. Rather than training your model on one set of data, you divide your data into several folds. This enables the model to train and validate on subsets that are different to assist you in identifying overfitting early and evaluating the performance of the model more steadily.
• L1 (Lasso) and L2 (Ridge) regularization approaches are a means of adding penalties to complex models. They add a penalty to the model’s loss function to stop it from relying too much on any one feature. This helps keep the feature weights small, which makes the model simpler and better at working with new data.
• Early stopping, especially in neural networks, is another effective technique. You monitor the validation loss during training and stop once it starts increasing, even if the training loss is still decreasing. This prevents the model from learning noise and overfitting in the later stages of training.
• You can use pruning to reduce overfitting in decision trees. Pruning works by cutting off branches that have little impact on the prediction accuracy, which helps simplify the model and improve its ability to generalize. In practice, libraries like scikit-learn provide built-in parameters to help with pruning, such as max_depth, min_samples_split, and min_samples_leaf.
• The other strategy is simplifying the models. Once the model you are using is overspecified, has too many parameters compared to the amount of data you possess, it is a good method if you scale it down. Overfitting can be slashed to great extents by switching from a high-degree polynomial model to a linear or low-degree polynomial model.
• Another suggestion you should not overlook is to consider whether you can increase your dataset size. Additional training examples train the model to learn general patterns, instead of just memorizing the details. Methods such as data augmentation of image classification (e.g., rotations, cropping, and flipping images) can artificially expand your dataset and augment its diversity.

The following is a little piece of code that demonstrates L2 regularization (Ridge Regression):

Example:

Output:

Explanation: Here, this demonstrates how Ridge regression aids in making the model generalize without overfitting, particularly when dealing with many features and noisy information. You’re quantifying how well your Ridge-regularized model performs, after preventing overfitting during training.

What is Underfitting in Machine Learning

Let us take an example analogy in real life: The situation of attempting to prepare a child to recognize animals, when only stick figures are presented. They might not learn enough to identify animals in real life. Likewise, an underfit model fails to learn the relation, as well as the pattern of the training data, which translates to poor prediction.

Underfitting happens when your machine learning model is too simple to capture the patterns in the data. It struggles to perform well not just on new, unseen data, but even on the training data itself. This usually means the model hasn’t learned enough from the data. You’ll notice this when both the training and testing errors are high. Underfitting often occurs when the model is not complex enough for the problem, for example, using a straight line to try and fit data that follows a curved pattern. It means your model is missing important signals and isn’t learning what it should.

Underfitting can be demonstrated with a simple model, by a tiny Python example:

Code:

Output:

Explanation: Here, this demonstration explains that some nonlinear models, like a sine wave, cannot be approximated by a simple linear regression model. The red line fails to capture the curve in the data, which is evidence of underfitting. The causes of underfitting are usually oversimplified models, insufficient training, or ignoring important features. It is the opposite of overfitting and should not be ignored.

Common Reasons Behind Underfitting in ML Algorithms

Underfitting is the reverse of overfitting. While overfitting means the model learns too much from the training data, underfitting means the model hasn’t learned enough. It’s important to watch out for it because an underfit model won’t perform well on either the training data or new data.

• Among the most frequent reasons, there is the usage of a too simple model for the problem. For example, applying a linear regression model to the data, which has a nonlinear relationship, the model is likely to be underfit. In this situation, it may not be possible to realize the simplest trends with your model.
• Lack of training is another most important cause. When you fit a model with insufficient epochs (in neural networks) or sufficient iterations (in tree-based models), then the model will not have sufficient time to learn. It is possible to stop early, which helps in avoiding overfitting, however, this could be counterproductive to a certain extent.
• Also, the feature selection is involved. Removing features that are important during the preprocessing process, or the inability to engineer valuable features, will deprive the model of critical data. So, it performs poorly not because it can’t do better, but because it doesn’t have enough data to learn from.
• Another frequent mistake is over-regularization. Methods such as L1 or L2 regularization can mediate against overfitting, yet extreme usage of these methods can over-restrict the model. This prevents it from learning patterns, which are, in fact, relevant.

Imagine you’re building a spam detector, but the only thing your model looks at is how long the email is. This means you’re ignoring other important details like how often certain words appear or who sent the email. Because of this, your model won’t learn what really makes an email spam, and it won’t perform well. This is called underfitting, where the model is too simple to catch the important patterns.

Proven Techniques to Prevent and Fix Overfitting

1. Use a More Complex Model: Underfitting often happens when the model is too simple to capture the underlying structure of the data. Switching from a linear model to something more complex, like a decision tree, random forest, or neural network, can help your model learn better patterns.
2. Train for More Epochs (in Neural Networks): If you’re using neural networks, underfitting might occur if the model hasn’t had enough time to learn. Increasing the number of training epochs allows the model to better capture the features in your data. Just be cautious to avoid overfitting.
3. Reduce Regularization Strength: Excessive use of L1 (Lasso) or L2 (Ridge) regularization can overly constrain your model, making it too simple. Reducing the regularization parameter (like alpha in Ridge) gives your model more flexibility to learn from data.
4. Add More Features or Use Feature Engineering: If your model is underfitting, it might be missing important inputs. Try adding more relevant features or engineering new ones (like polynomial features or interactions) to help the model learn deeper patterns.
5. Ensure Proper Data Preprocessing: Incorrect scaling, normalization, or handling of categorical variables can make it hard for your model to learn. Always preprocess your data properly, which includes encoding, assigning missing values, and scaling numerical data when needed.

Here, we are providing you with a code to prevent underfitting. We will use L2 Regularization (Ridge Regression) and Cross-Validation with scikit-learn:

Output:

Explanation: Here, you will see a graph with training and testing R² scores plotted against varying alpha values. The sweet spot is where both training and testing scores are balanced and reasonably high, which indicates good generalization. Now:

• When alpha is too small (close to 0), the model overfits, meaning high training accuracy but poor test performance.
• When alpha is too large, the model underfits, meaning low training and testing accuracy.

Bias-Variance Tradeoff

In machine learning, the bias-variance tradeoff is one of the fundamental concepts that determines how to balance underfitting and overfitting. It states the reason why models do not work well, either when they are too basic or when they are too complicated. To create a generalization model, there must be a balance between bias and variance.

Bias is the error that arises due to making naive assumptions in a model, so that it overlooks the patterns, and underfits the data. High bias typically leads to both high training and testing errors, because the model is too simple to capture the underlying patterns in the data.

Variance refers to how much a model’s predictions change when it’s trained on different data. A model with high variance is too sensitive to the training data and may not perform well on new data. High variance implies the model is too closely fitted to the training data, has poor generalization, and will overfit.

This is a breakdown of the tradeoff:

• Underfitting = High bias, Low variance. You are too simplistic.
• Overfitting = Low bias, high variance. Your model is too complicated.
• Normalized bias and variance = Good generalizations. This is what will be achieved.

We take a look at the current example relating to the regression of a polynomial:

Output:

Explanation:

• Degree 1 (high bias): Fails to capture the sine wave pattern, and it runs straight under the curve.
• Degree 15 (high variance): Overfits, acquires noise, as well as variance in training the data.
• Degree 4: A balanced model that better (naturally) fits a sine curve.

The bias-variance tradeoff can explain to you how you can tune your models. One form of this balance is hyperparameter adjustment, the use of suitable algorithms, and regularization methods.

Overfitting vs. Underfitting

Best Practices for Model Evaluation and Generalization in ML

• Employ CV Methods: Use k-fold or stratification cross-validation to verify that your model has been evaluated on different data partitions, and thus it can be used to achieve a better fit to new data.
• Retain a Different Test Set: A thoroughly separate test set (no use during training or validation) should always be available to judge the final performance objectively.
• Examine learning curves: Plot training and validation loss to see an indication of overfitting or underfitting. Curves that are diverging mean that regularization or model tuning is required.
• Apply Regularization: Methods such as L1, L2 regularization, or dropout (in neural nets) have the aim of avoiding overfitting and enhancing generalizability.
• Use multiple evaluation metrics: don’t rely only on accuracy, but rely on precision, recall, F1-score, and AUC, according to the type of problems, to obtain a balanced perspective of performance.

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Conclusion

It is important to know and manage problems such as overfitting and underfitting to construct reliable and correct machine learning models. Overfitting happens when your model memorizes the training data but struggles with new, unseen data. Underfitting is the opposite, where the model doesn’t learn enough from the data and misses important patterns. The key is to find the right balance between the two, which is known as the bias-variance tradeoff. This means making sure your model learns well, but also works on new data. By mastering these concepts and techniques, you’ll be well-equipped to build models that are reliable in real-world scenarios.

Overfitting and Underfitting- FAQs