7.7 Principles of Supervised Learning: Overfitting and Underfitting
Supervised learning is one of the fundamental pillars of Machine Learning (ML), where the algorithm learns from labeled data to make predictions or classifications. However, two of the main challenges that arise when training supervised learning models are overfitting and underfitting. These concepts are crucial for developing efficient and reliable models.
What is Overfitting?
Overfitting occurs when an ML model learns the details and noise in training data to such an extent that it becomes overly complex and ends up performing poorly on new, previously unseen data. This means that the model overfitted the training data, capturing patterns that are not generalizable to other datasets.
Causes of Overfitting
- Model Complexity: Models with many parameters, such as deep neural networks, are particularly prone to overfitting as they have the ability to learn very specific patterns in the training data.
- Little Data: Having a small training data set can lead to a model that does not have enough data to learn truly generalizable patterns.
- Noise in the Data: If the training data contains a lot of noise, the model may end up learning this noise as if it were meaningful features.
How to Avoid Overfitting
- Regularization: Techniques such as L1 and L2 add a penalty term to the model cost function to discourage excessive complexity.
- Cross-Validation: Using cross-validation allows you to evaluate how the model generalizes to an independent dataset during training.
- Prune the Model: Reduce model complexity by removing layers or neurons in neural networks, or choosing simpler models.
- Early Stopping: Stop training as soon as performance on a validation data set starts to deteriorate.
- Data Augmentation: Create new training data artificially through techniques such as rotating, shifting or mirroring images.
What is Underfitting?
Underfitting is the opposite of overfitting and occurs when a model is too simple to capture the complexity of the data. As a result, the model does not learn the underlying patterns in the training data well enough, leading to poor performance on both training and test data.
Causes of Underfitting
- Too Simple Model: A model with few parameters or that is not complex enough to capture the structure of the data.
- Inadequate Features: Using a set of features that do not capture important information from the data.
- Insufficient Training: Stopping training too early before the model has had a chance to properly learn from the data.
How to Avoid Underfitting
- Increase Model Complexity: Choosing a more complex model or adding more parameters can help better capture the structure of the data.
- Feature Engineering: Create new features or transform existing ones to better represent the information in the data.
- More Training: Allowing the model to train longer can help it learn patterns in the training data.
Balancing Overfitting and Underfitting
Finding the right balance between avoiding overfitting and underfitting is both an art and a science. The goal is to achieve a good compromise between the model's ability to generalize to new data (avoiding overfitting) and its ability to capture sufficient information from the training data (avoiding underfitting). This is often achieved through experimentation and fine-tuning the model's hyperparameters.
In summary, understanding and mitigating overfitting and underfitting is essential to creating robust and reliable machine learning models. By applying techniques such as regularization, cross-validation, and feature engineering, we can significantly improve a model's ability to make accurate predictions on new and unknown data.
