What is Underfitting and Overfitting in Machine Learning?

• Blockchain Council
• September 22, 2024

Machine learning models help detect patterns in data and provide predictions. However, issues may arise occasionally, causing problems like underfitting or overfitting. Both of these issues can affect a model’s performance and accuracy.

What is Underfitting?

Underfitting happens when a model is too simple to grasp the key patterns in the data. It typically occurs when the model doesn’t learn enough from the training data, leading to weak results on both the training and test sets. Essentially, the model lacks the necessary complexity to fully understand the relationship between input and output data.

For example, if you use a linear regression model on data with complex, non-linear patterns, the linear model will produce a straight line. This approach misses out on capturing detailed trends, leading to high errors and low accuracy, which is a clear case of underfitting.

Signs of Underfitting:

• High errors in both training and validation that are close in value.
• Simplified decision boundaries that don’t effectively separate data points in classification tasks.
• Poor accuracy on training and test data, indicating insufficient learning.

Common Causes of Underfitting:

• Choosing overly basic models that don’t match the data’s complexity.
• Not allowing enough training time or improperly adjusting model parameters.
• Applying too much regularization, which limits the model’s ability to capture detailed patterns.

What is Overfitting?

Overfitting occurs when a model becomes overly focused on the training data, learning even small errors and irregular patterns. This occurs when the model becomes overly complicated, fitting the training data closely but struggling to generalize to new data. As a result, it works well with the training data but struggles with new, unseen information, which causes wrong predictions.

For instance, a deep neural network might overlearn the details of the training data, capturing even minor noise, which leads to overly intricate decision boundaries. These boundaries fail when applied to new data, causing the model to perform poorly in practical situations.

Signs of Overfitting:

• Low error on training data but high error on validation data.
• Complex or irregular decision boundaries in classification tasks.
• Strong performance on training data but disappointing results on test data.

Common Causes of Overfitting:

• Models with too many parameters for the given data tend to overfit.
• Limited data for training, causing the model to rely heavily on the specific examples it has seen.
• Not enough regularization, allowing the model to become too flexible.

Bias-Variance Tradeoff

Underfitting and overfitting are linked to the balance between bias and variance in a model. Bias refers to errors from simplistic assumptions, while variance relates to errors from being too sensitive to small changes in data. A model with high bias tends to underfit, while high variance leads to overfitting. Balancing these factors is crucial for building effective models.

How to Prevent Underfitting

Preventing underfitting involves making the model better at learning from data. Here are some strategies:

• Increase Model Complexity: Use more advanced algorithms or add additional layers to neural networks to better capture complex patterns.
• Extend Training Time: Allow the model to train longer to better understand data patterns, but avoid overfitting.
• Reduce Regularization: Lower the level of regularization to avoid overly simplifying the model.
• Enhance Feature Set: Add relevant features or create new ones to give the model more information.

How to Prevent Overfitting

To combat overfitting, models need to be simplified to improve their ability to handle new data. Consider these methods:

• Simplify the Model: Reduce the number of parameters, layers, or features to decrease complexity, helping the model focus on important trends rather than memorizing specific details.
• Increase Training Data: More diverse data helps the model learn broader patterns, reducing reliance on specific examples.
• Apply Regularization Techniques: Methods like L1 (Lasso) or L2 (Ridge) regularization help manage complexity, making the model more robust.
• Use Cross-Validation: This approach evaluates model performance across multiple subsets of data, offering a better measure of its ability to generalize.
• Data Augmentation: For tasks like image classification, techniques such as flipping or rotating images introduce variation without needing more data.
• Early Stopping: It involves watching the model’s validation performance and halting training when overfitting begins to show.

Balancing Underfitting and Overfitting

Balancing underfitting and overfitting is key to developing successful models. It involves fine-tuning complexity, regularization, and training duration to achieve solid performance on both training and validation data.

Strategies to Achieve Balance

• Cross-Validation: Methods, such as k-fold, evaluate the model on various subsets, which helps lower the chance of overfitting.
• Feature Selection: Remove unnecessary features to combat overfitting; add useful features to address underfitting.
• Data Augmentation: Involves adding variations like rotating images, which improves the model’s ability to generalize.
• Ensemble Methods: Techniques like bagging (e.g., Random Forests) and boosting (e.g., Gradient Boosting Machines) can help address both underfitting and overfitting.

Conclusion

Underfitting and overfitting represent common challenges in machine learning, showing the extremes of model learning. A balanced approach that considers the model’s complexity and the data’s nature is essential. By using strategies to handle these issues, models can perform well on training data and effectively generalize to new situations.