Here’s Everything You Need To Know About Overfitting
Glossary

Here’s Everything You Need To Know About Overfitting

March 5, 2024 | By
What Is Overfitting? Overfitting is a common problem in machine learning that occurs when a model becomes too aligned with the training data. This means the model captures even the random noise and idiosyncrasies in the data, rather than the underlying patterns. As a result, the model performs well on the training data but fails to generalise new, unseen data. Why Does Overfitting Happen? Overfitting in machine learning arises due to several factors that lead the model to prioritise the specific details and noise within the training data rather than capturing the underlying generalisable patterns: Limited Training Data: When the training data is small, it might not represent the full spectrum of possible scenarios the model might encounter in the real world. The model, lacking diverse examples, ends up fitting itself too closely to the specific data points it has seen, including any random errors or quirks present in that limited data. This makes it unable to adapt to unseen data that might follow slightly different patterns. Model Complexity: A highly complex model with many parameters has a greater capacity to learn intricate details. While this can be beneficial for capturing complex relationships in the data, it also increases the risk of overfitting. The model might become overly sensitive to the specific features and patterns in the training data, including irrelevant noise, and struggle to generalise to new data that doesn't perfectly match the training examples. Overtraining: Training the model for too long can also lead to overfitting. As the model continues to learn from the training data, it might start memorising the specific data points and their associated outputs, including any noise or errors present. This can lead to the model losing its ability to generalise and perform well on unseen data. Noisy Data: The presence of noise or irrelevant information in the training data can further contribute to overfitting. The model might struggle to distinguish between the actual patterns and the random noise, leading it to learn irrelevant details that don't generalise well to new data. What Are The Symptoms Of Overfitting? Overfitting in machine learning models exhibits several tell-tale signs that indicate the model might be memorising the training data instead of learning the underlying patterns. Here are some key symptoms to watch for: Significant Disparity Between Training And Testing Performance: If the model achieves very high accuracy on the training data but performs poorly on unseen data from a separate test set, it's a strong indication of overfitting. This suggests the model has memorised the training data but hasn't learned generalisable rules. High Training Accuracy With Increasing Model Complexity: As you increase the complexity of your model (for example, by adding more layers in a neural network), it's expected to perform better on the training data. However, if the training accuracy continues to rise significantly even with increasing complexity, while the test set performance stagnates or worsens, it suggests overfitting. The model might be capturing irrelevant details and noise in the training data instead of learning patterns. High Variance In Performance: Models prone to overfitting often exhibit high variance in their performance, meaning their performance can fluctuate significantly on different test sets drawn from the same population. This inconsistency arises because the model is overly sensitive to the specific data points it encounters, and small changes in the data can lead to large changes in its predictions. Learning Curves: Plotting learning curves, which show the model's performance on both the training and test data as the training process progresses, can be a valuable tool for identifying overfitting. In overfitting scenarios, the training accuracy curve will typically continue to improve even as the test set performance plateaus or starts to decline. This indicates the model is no longer generalising and is simply memorising the training data. Domain Knowledge Contradiction: If the model's predictions contradict your understanding of the problem domain or seem nonsensical based on real-world knowledge, it might be a sign of overfitting. The model might be capturing irrelevant patterns or noise in the data that don't reflect the actual underlying relationships. How Can Overfitting Be Fixed? Overfitting in machine learning, while a common challenge, can be addressed through various techniques that aim to prevent the model from memorising the training data and instead focus on learning generalisable patterns. Here are some effective methods to combat overfitting: Data-Centric Approaches Increase Training Data Size: Having a larger and more diverse dataset provides the model with a broader range of examples, allowing it to capture the underlying patterns more effectively and reducing the likelihood of overfitting specific training data points. Data Augmentation: This technique artificially expands the training data by creating new data points through transformations like flipping, rotating, or scaling existing data. This helps the model learn from variations within the data and become more robust to unseen examples. Feature Selection: By identifying and removing irrelevant or redundant features from the training data, you can reduce the model's complexity and its capacity to overfit to noise or unimportant features. Model-Centric Approaches Regularisation: This group of techniques introduces penalties to the model's loss function during training, discouraging it from learning overly complex patterns that might not generalise well. Common methods include L1 and L2 regularisation, which penalise the model for having large weights or parameters, pushing it towards simpler solutions. Early Stopping: This technique monitors the model's performance on a validation set during training. If the performance on the validation set starts to decline after a certain point, even while the training accuracy continues to improve, it suggests overfitting. Early stopping halts the training process at this point, preventing the model from memorising noise and improving its generalisability. Model Simplification: Reducing the model's complexity by using fewer parameters or layers can help prevent overfitting. This can be achieved through techniques like choosing simpler model architectures, pruning unnecessary connections, or reducing the number of features used. Ensemble Methods: Bagging And Boosting: These techniques combine predictions from multiple, slightly different models to create a more robust ensemble model. By averaging or strategically weighting the predictions from these individual models, ensemble methods can help reduce the variance associated with overfitting and improve the overall generalisability of the final model.

Overfitting is a common problem in machine learning that occurs when a model becomes too aligned with the training data.

Table of Contents

What Is Overfitting?

Overfitting is a common problem in machine learning that occurs when a model becomes too aligned with the training data. This means the model captures even the random noise and idiosyncrasies in the data, rather than the underlying patterns. As a result, the model performs well on the training data but fails to generalise new, unseen data.

Why Does Overfitting Happen?

Overfitting in machine learning arises due to several factors that lead the model to prioritise the specific details and noise within the training data rather than capturing the underlying generalisable patterns:

  • Limited Training Data: When the training data is small, it might not represent the full spectrum of possible scenarios the model might encounter in the real world. The model, lacking diverse examples, ends up fitting itself too closely to the specific data points it has seen, including any random errors or quirks present in that limited data. This makes it unable to adapt to unseen data that might follow slightly different patterns.
  • Model Complexity: A highly complex model with many parameters has a greater capacity to learn intricate details. While this can be beneficial for capturing complex relationships in the data, it also increases the risk of overfitting. The model might become overly sensitive to the specific features and patterns in the training data, including irrelevant noise, and struggle to generalise to new data that doesn’t perfectly match the training examples.
  • Overtraining: Training the model for too long can also lead to overfitting. As the model continues to learn from the training data, it might start memorising the specific data points and their associated outputs, including any noise or errors present. This can lead to the model losing its ability to generalise and perform well on unseen data.
  • Noisy Data: The presence of noise or irrelevant information in the training data can further contribute to overfitting. The model might struggle to distinguish between the actual patterns and the random noise, leading it to learn irrelevant details that don’t generalise well to new data.

What Are The Symptoms Of Overfitting?

Overfitting in machine learning models exhibits several tell-tale signs that indicate the model might be memorising the training data instead of learning the underlying patterns. Here are some key symptoms to watch for:

  • Significant Disparity Between Training And Testing Performance: If the model achieves very high accuracy on the training data but performs poorly on unseen data from a separate test set, it’s a strong indication of overfitting. This suggests the model has memorised the training data but hasn’t learned generalisable rules.
  • High Training Accuracy With Increasing Model Complexity: As you increase the complexity of your model (for example, by adding more layers in a neural network), it’s expected to perform better on the training data. However, if the training accuracy continues to rise significantly even with increasing complexity, while the test set performance stagnates or worsens, it suggests overfitting. The model might be capturing irrelevant details and noise in the training data instead of learning patterns.
  • High Variance In Performance: Models prone to overfitting often exhibit high variance in their performance, meaning their performance can fluctuate significantly on different test sets drawn from the same population. This inconsistency arises because the model is overly sensitive to the specific data points it encounters, and small changes in the data can lead to large changes in its predictions.
  • Learning Curves: Plotting learning curves, which show the model’s performance on both the training and test data as the training process progresses, can be a valuable tool for identifying overfitting. In overfitting scenarios, the training accuracy curve will typically continue to improve even as the test set performance plateaus or starts to decline. This indicates the model is no longer generalising and is simply memorising the training data.
  • Domain Knowledge Contradiction: If the model’s predictions contradict your understanding of the problem domain or seem nonsensical based on real-world knowledge, it might be a sign of overfitting. The model might be capturing irrelevant patterns or noise in the data that don’t reflect the actual underlying relationships.

How Can Overfitting Be Fixed?

Overfitting in machine learning, while a common challenge, can be addressed through various techniques that aim to prevent the model from memorising the training data and instead focus on learning generalisable patterns. Here are some effective methods to combat overfitting:

Data-Centric Approaches

  • Increase Training Data Size: Having a larger and more diverse dataset provides the model with a broader range of examples, allowing it to capture the underlying patterns more effectively and reducing the likelihood of overfitting specific training data points.
  • Data Augmentation: This technique artificially expands the training data by creating new data points through transformations like flipping, rotating, or scaling existing data. This helps the model learn from variations within the data and become more robust to unseen examples.
  • Feature Selection: By identifying and removing irrelevant or redundant features from the training data, you can reduce the model’s complexity and its capacity to overfit to noise or unimportant features.

Model-Centric Approaches

  • Regularisation: This group of techniques introduces penalties to the model’s loss function during training, discouraging it from learning overly complex patterns that might not generalise well. Common methods include L1 and L2 regularisation, which penalise the model for having large weights or parameters, pushing it towards simpler solutions.
  • Early Stopping: This technique monitors the model’s performance on a validation set during training. If the performance on the validation set starts to decline after a certain point, even while the training accuracy continues to improve, it suggests overfitting. Early stopping halts the training process at this point, preventing the model from memorising noise and improving its generalisability.
  • Model Simplification: Reducing the model’s complexity by using fewer parameters or layers can help prevent overfitting. This can be achieved through techniques like choosing simpler model architectures, pruning unnecessary connections, or reducing the number of features used.

Ensemble Methods

  • Bagging And Boosting: These techniques combine predictions from multiple, slightly different models to create a more robust ensemble model. By averaging or strategically weighting the predictions from these individual models, ensemble methods can help reduce the variance associated with overfitting and improve the overall generalisability of the final model.