A Guide to Overfitting and Underfitting in Machine Learning
Yes, machine learning is a powerful tool that enables computers to learn from data and make predictions or decisions without being explicitly programmed. However, accurate predictions and correct decisions are based on the ML model understanding patterns and being able to generalize to new, unseen data. Which leads us to overfitting and underfitting in machine learning.
Models that are too simplistic may fail to capture the underlying patterns in the data, while overly complex models risk fitting the training data perfectly but struggle to generalize. This is a delicate balance that is difficult to get correct. In this guide, we will explore how to achieve this balance by examining overfitting and underfitting, understanding the causes, and discuss how to mitigate them in machine learning models.
What Is Overfitting and Underfitting in Machine Learning?
Essentially, overfitting in machine learning is when a model learns its training data too well, capturing noise, random fluctuations, or outliers in the data, but does not understand the underlying patterns. As a result, the model performs exceptionally well on the training data but struggles to generalize to new, unseen data – which is the ultimate purpose of ML models.
Overfit models have a high variance, meaning they are sensitive to small variations in the training data, leading to poor performance on test or validation datasets. You can think of overfitting as a student who has memorized the answers to a few specific questions but cannot answer any questions beyond those.
Underfitting, on the other hand, occurs when a machine learning model is too simplistic to capture the underlying patterns in the data. It results in poor performance on both the training data and new, unseen data.
An underfit model lacks the capacity to learn the complexities within the data, resulting in consistently high errors. Imagine underfitting as a student who didn’t pay attention in class, didn’t study the material, and consequently performs poorly on all types of questions, including the ones they were explicitly taught.
How to Identify Overfitting and Underfitting?
Identifying overfitting and underfitting is the first step towards building robust machine learning models. These issues are primarily assessed by analyzing the differences between the training error and the validation or test error.
The Role of Training and Validation/Test Errors
In the realm of machine learning, every model is trained on a dataset, and its performance is evaluated on unseen data, typically called a test or validation dataset. This separation helps you understand how well a model generalizes to new, unseen instances.
The training error represents how well the model fits the training data, while the validation/test error measures its performance on new, unseen data. The key distinction between overfitting and underfitting lies in how these two errors behave.
Indicators of Overfitting and Underfitting
To identify overfitting and underfitting, it’s essential to be aware of their indicators:
- Overfitting Indicators:
- Low Training Error, High Validation/Test Error: When the training error is significantly lower than the validation/test error, it’s a sign of overfitting.
- Unstable Model: An overfit model may exhibit instability, showing different behaviors on small changes in the training data.
- Underfitting Indicators:
- High Training Error, High Validation/Test Error: In underfitting, both training and validation/test errors remain high and close to each other.
- Overly Simplistic Model: An underfit model typically lacks the complexity to represent the underlying data distribution.
Finding the Balance
Data scientists aim to strike a balance between overfitting and underfitting. This is a critical aspect of model building. The goal is to create models that generalize well to new data while still capturing the essential patterns within the training data. Achieving this balance is a fundamental challenge in machine learning, and it requires a deep understanding of the data and model characteristics.
Causes of Overfitting and Underfitting in Machine Learning Models
Understanding the causes of overfitting and underfitting is crucial for effectively addressing these issues in your machine learning models.
Complex Model Structure
One of the primary causes of overfitting is model complexity. In machine learning, the model’s complexity is often related to the number of features or parameters it possesses.
Complex models have a higher capacity to fit the training data perfectly, but they struggle to generalize to new data because they may end up capturing noise instead of true patterns.
Insufficient Training Dataset
The size and quality of the training dataset play a significant role in overfitting and underfitting. Having an insufficient training dataset, either due to a small size or a lack of diversity, can lead to both issues.
A small training dataset lacks the diversity needed to represent the underlying data distribution accurately. As a result, the model may overfit, as it attempts to fit the limited training instances too closely. Conversely, an underfit model may occur if the training dataset is too small to learn the essential patterns.
Outliers in the Training Data
Outliers – data points that significantly deviate from the majority of the data, can have a profound impact on machine learning models. They can be a source of both overfitting and underfitting.
Outliers may lead to overfitting if the model tries to fit them perfectly, thus capturing their noise rather than the actual patterns in the data.
On the other hand, if the model ignores the outliers entirely, it might underfit, as it fails to capture these important data points. Identifying and handling outliers in the training data is crucial to prevent these issues.
Non-Representative Training Data
Non-representative training data refers to a dataset that doesn’t accurately reflect the diversity and distribution of the data you intend to make predictions on. This issue can adversely affect the performance and accuracy of your ML model.
If your training data is biased or unrepresentative of the broader dataset, the model may fail to generalize to unseen instances or make inaccurate predictions. To mitigate this, it’s essential to use a diverse and unbiased training dataset that reflects the true characteristics of the problem you’re trying to solve.
Training Process Duration
The duration of training also has an impact on overfitting and underfitting in machine learning models. Increasing the duration of the training process can lead to overtraining which causes overfitting, as the model might start to memorize the training data rather than learning the underlying patterns. This is a clear consequence of excessive training.
To address this issue, it’s important to find the optimal stop point during the training process, where the model achieves a balance between fitting the training data and generalizing to new data. This requires careful monitoring and tuning during model training.
Effects of Overfitting and Underfitting on Machine Learning Models
The impact of overfitting and underfitting extends to the accuracy, performance, and prediction errors, all of which are essential characteristics of machine learning models.
Accuracy and Performance
Model accuracy and performance are critical metrics in evaluating an ML model’s effectiveness and these aspects are directly influenced by overfitting and underfitting.
- Bias and Variance: In the context of machine learning, bias and variance are two critical elements that affect model performance. Bias is the error introduced by approximating a real-world problem with a simplified model, and it often leads to underfitting. Variance, on the other hand, is the error introduced by a model’s sensitivity to small fluctuations in the training data and is a common cause of overfitting.
- Metrics: To assess the accuracy and performance of a model, various metrics are employed, including training accuracy, test accuracy, and the crucial concept of generalization error. Generalization error measures how well a model performs on unseen data and is a direct reflection of overfitting and underfitting.
- Bias-Variance Tradeoff: Achieving the right balance between bias and variance is essential for optimal model performance. This is known as the bias-variance tradeoff, and it involves making trade-offs to minimize both bias and variance to create a model that generalizes effectively.
The prediction error is a concept that directly contributes to a model’s generalization error. It encompasses various sources of error, including bias error, variance error, and irreducible error.
As we mentioned above, a bias error occurs when a model is too simplistic and fails to capture the underlying patterns in the data. This results in systematic errors that consistently misrepresent the data.
A variance error arises when a model is overly complex and captures noise in the training data, leading to errors when generalizing to new data. Finally, an irreducible error is a source of error that cannot be reduced by improving the model. It stems from noise in the data and the inherent uncertainty in real-world processes. Low prediction accuracy is directly related to poor model performance.
How Can You Avoid Overfitting and Underfitting in Machine Learning Models?
In truth, it’s not easy to avoid overfitting and underfitting in machine learning models. You need high-quality training data sets, a good base model, and iterative human monitoring during training. This is something Aya Data can help with.
Aya provides services across the entire AI data chain – from data acquisition to bespoke ML model creation. We can create a high-quality data set for your desired ML models and help you deploy them. If you are interested in discussing how Aya Data can with your ML project, feel free to schedule a free consultation with one of our experts.