Overfitting is a common phenomenon in machine learning where a model performs exceptionally well on the training data but fails to generalize effectively to new, unseen data. In essence, the model becomes too specialized in capturing the quirks and noise present in the training set, losing its ability to discern the underlying patterns that should generalize well.
Overfitting Illustration
Let's consider a scenario where three chefs, A, B, and C, are learning to cook a specific dish. Each chef has a different approach to learning and preparing the recipe.
Chef A focuses only on a few key ingredients and techniques and ignores the rest. Then, he becomes exceptionally skilled in those limited aspects of cooking the dish. However, if the recipe requires additional ingredients or techniques, Chef A would struggle to adapt and may produce a subpar dish.
Chef B, on the other hand, meticulously memorizes every single detail of the recipe, including the precise measurements and steps. He has a fantastic memory and can reproduce the dish exactly as written. However, if any slight variation or unexpected ingredient is introduced, Chef B may find it challenging to adjust and may struggle to create a satisfactory outcome.
Chef C takes a well-rounded approach. He not only studies the recipe but also experiments with different variations, techniques, and ingredients. Chef C practices extensively and understands the underlying principles and flavors. As a result, he can prepare the dish consistently well, even when faced with slight modifications or unfamiliar ingredients.
In this example, Chef A represents underfitting. He has limited knowledge and can only perform well in specific circumstances. Chef B represents overfitting, as they have memorized the recipe but struggle with variations and unexpected inputs. Chef C represents a well-fit model that generalizes well and performs consistently, adapting to different situations.
Similarly, in machine learning, an underfit model cannot capture the complexity of the data and performs poorly. An overfit model memorizes the training data too precisely, failing to generalize to new, unseen data. A well-fit model, like Chef C, strikes a balance, capturing the essential patterns and features while adapting to variations in the data and performing well on both training and unseen datasets.
How Overfitting Occurs?
Overfitting typically happens when the model becomes excessively complex, having too many parameters relative to the available training data. With excessive complexity, the model can essentially memorize the training examples, including the random fluctuations and noise, rather than learning the essential underlying features.
Additionally, overfitting can occur when the training data is unrepresentative of the target population or lacks diversity. If the training set is biased or does not adequately cover the range of potential scenarios, the model may develop biases or blind spots that hinder its generalization capability.
How can you detect Overfitting?
There is always noise and imprecision in data. Overfitting happens when the model starts learning those noises and imprecisions, which leads to incorrect models.
That’s why, to detect overfitting, you have to compare the loss between the train data and the validation data. When overfitting happens, the loss increases, and the validation data’s loss is way more important than the train data loss.
How to prevent Overfitting from happening?
- Cross-Validation and Data Splitting: Splitting the available data into separate training and validation sets is crucial. The training set is used to train the model, while the validation set helps assess its performance on unseen data. By evaluating the model's performance on the validation set, it is possible to detect overfitting and make adjustments accordingly.
- Regularization Techniques: Regularization methods, such as L1 and L2 regularization, add penalty terms to the model's objective function, discouraging excessive parameter values. This helps prevent the model from becoming overly sensitive to individual data points and encourages it to focus on the most relevant features.
- Feature Selection and Dimensionality Reduction: Careful feature selection or dimensionality reduction techniques, such as principal component analysis (PCA) or feature importance ranking, can help reduce the complexity of the model and focus on the most informative features. By removing irrelevant or redundant features, the risk of overfitting decreases. However, this method doesn’t really make sense in deep learning due to the use of Convolutional Neural Networks (CNN).
- Data Augmentation: Data augmentation involves artificially increasing the size of the training set by applying transformations, such as rotations, translations, and scaling, to the existing data. This technique introduces additional variations, making the model more robust and less prone to overfitting.
- Early Stopping: Monitoring the model's performance on the validation set during training allows for early stopping when overfitting is detected. Training can be halted when the model's performance on the validation set starts to deteriorate, preventing it from memorizing the training data excessively.
- Ensemble Methods: Ensemble methods combine multiple but simple models to make predictions. By training several less-complex models with different initial conditions or algorithms and combining their predictions, the ensemble can generalize better than any individual model, and models are less prone to overfitting.
- Cross-Validation and Hyperparameter Tuning: Cross-validation techniques, such as k-fold cross-validation, help assess a model's performance on different subsets of the data. Hyperparameter tuning, using techniques like grid search or Bayesian optimization, allows for finding the optimal hyperparameter values that yield better generalization and minimize overfitting.
Zoom on the k-Cross-Validation technique:
This technique consists in dividing the dataset into k equal-sized subsets or “folds”. The model is trained and evaluated k times, each time using a different fold as the validation set and the remaining folds for training. The performance metrics obtained from each iteration are averaged to estimate the model’s overall performance.
To help you fight overfitting learning, Picsellia provides ready-to-use, customizable, data augmentation processings. But there is more, the platform has an experiment tracking system that tells the user when to stop to have the best balance between complexity and generalization, saving time and resources in the process.
Conclusion
Overfitting is a usual challenge in machine learning, where models become too specialized in the training data and fail to generalize effectively. Fortunately, strategies exist to avoid overfitting. And Picsellia's end-to-end MLops platform helps in setting up those strategies as it offers a comprehensive solution to mitigate overfitting challenges, enabling organizations to overcome the limitations posed by poor generalization.
