In machine learning, model performance is critical to making accurate predictions. However, models often face two major issues: overfitting and underfitting. While overfitting occurs when a model learns noise instead of patterns, underfitting happens when a model is too simple to capture the underlying structure in the data.
In this article, we will explore what underfitting is, why it occurs, how to detect it, and how to prevent it to ensure machine learning models achieve the best possible performance.
What is Underfitting?
Underfitting in machine learning occurs when a model is too simplistic to capture the relationships within a dataset. As a result, the model fails to learn from the training data and performs poorly on both training and testing sets.
Key Characteristics of Underfitting:
✔ High bias – The model makes strong assumptions and fails to learn patterns. ✔ Poor training accuracy – The model does not fit the training data well. ✔ Poor testing accuracy – The model fails to generalize to new data. ✔ Low variance – The model does not react much to different training sets.
Example of Underfitting:
Consider a linear regression model trying to predict house prices based on features like square footage and location. If the model only considers square footage and ignores other factors, it may fail to capture the real relationship between variables, leading to poor predictions.
Causes of Underfitting
Several factors contribute to underfitting in machine learning models:
1. Choosing an Oversimplified Model
If a model is too simple (e.g., using a linear model for non-linear data), it cannot capture complex patterns.
Example: Using linear regression when the data exhibits a non-linear trend.
2. Insufficient Training
If a model is trained for too few epochs (in deep learning) or with insufficient iterations, it may not learn the underlying patterns in the data.
Example: Training a neural network for only 5 epochs when 50 epochs are required for convergence.
3. Lack of Relevant Features
Using too few or irrelevant features can cause underfitting, as the model lacks enough information to make accurate predictions.
Example: Predicting customer churn using only demographic data while ignoring behavioral patterns.
4. Excessive Regularization
Regularization techniques like L1 (Lasso) and L2 (Ridge) regression help prevent overfitting, but excessive regularization can force the model to ignore important patterns, leading to underfitting.
Example: Setting a very high L2 regularization parameter in a logistic regression model.
5. Using Too Little Training Data
A small dataset may not provide enough examples for the model to learn meaningful relationships, especially in deep learning applications.
Example: Training a convolutional neural network (CNN) on only 100 images when thousands are needed.
How to Detect Underfitting
To identify underfitting, monitor these key indicators:
1. Training and Validation Accuracy
- If both training and validation accuracy are low, the model is likely underfitting.
- In contrast, overfitting occurs when training accuracy is high, but validation accuracy is low.
2. High Bias in Learning Curves
- If a learning curve flattens out at a low accuracy level and does not improve with more training, underfitting is likely.
3. High Error Rates in Both Training and Testing
- If both training error and testing error are high, the model is too simple to fit the data properly.
4. Residual Plots in Regression Models
- If residuals (errors) show clear patterns instead of being randomly distributed, it indicates the model is missing key relationships in the data.
How to Prevent Underfitting
1. Choose a More Complex Model
If a simple model is underperforming, try using a more advanced model that can capture complex patterns.
✔ Use polynomial regression instead of linear regression for non-linear relationships. ✔ Use deep neural networks instead of shallow networks for image and text processing. ✔ Use decision trees instead of linear models for structured data with non-linearity.
2. Train the Model Longer
For deep learning models, training for more epochs allows the model to learn better representations.
✔ Increase the number of epochs or iterations in gradient-based learning algorithms. ✔ Monitor training curves to ensure loss continues decreasing.
3. Add More Features
If the dataset lacks important information, adding more relevant features can improve performance.
✔ Feature engineering techniques like one-hot encoding, polynomial features, or embeddings can help. ✔ Using domain knowledge to identify missing variables improves predictions.
4. Reduce Regularization Strength
If L1 (Lasso) or L2 (Ridge) regularization is too high, it may suppress useful features.
✔ Decrease the regularization parameter (λ) to allow the model to learn more. ✔ Use cross-validation to find an optimal balance between underfitting and overfitting.
5. Increase Training Data
A small dataset can lead to underfitting, so increasing the number of examples helps models generalize better.
✔ Collect more real-world data if possible. ✔ Use data augmentation (e.g., rotating images in image classification) to artificially increase dataset size. ✔ Apply transfer learning by using pre-trained models instead of training from scratch.
Underfitting vs Overfitting
| Aspect | Underfitting | Overfitting |
|---|---|---|
| Definition | Model is too simple to capture patterns. | Model memorizes noise instead of generalizing. |
| Training Accuracy | Low | High |
| Testing Accuracy | Low | Low |
| Error Rates | High in both training and testing | Low in training, high in testing |
| Bias vs Variance | High bias, low variance | Low bias, high variance |
| Solution | Use a more complex model, train longer, add features | Use regularization, reduce model complexity, increase data |
Conclusion
Underfitting in machine learning occurs when a model is too simplistic to learn meaningful patterns from data, leading to poor performance on both training and test datasets.
Key Takeaways:
✔ Underfitting is caused by overly simple models, insufficient training, lack of features, excessive regularization, or small datasets. ✔ It can be detected through low training and testing accuracy, high bias, and residual analysis. ✔ Prevent underfitting by using more complex models, training longer, increasing data, reducing regularization, and improving feature engineering. ✔ Striking a balance between underfitting and overfitting is crucial for building a well-generalized model.
By understanding and addressing underfitting, data scientists can build machine learning models that accurately capture data patterns and perform well in real-world applications.