Overfitting is a major challenge in the field of machine learning. This phenomenon occurs when a model learns too well from training data, to the point of losing its ability to generalize on new data. Understanding and solving this problem has an influence on the performance and reliability of artificial intelligence systems in many sectors.
💡This article explores the fundamental aspects of the concept of overfitting and presents effective strategies for reducing it. It also examines methods for evaluating and monitoring this phenomenon throughout the development process of artificial intelligence models. Through this article, you'll be able to learn the basics for creating more robust, high-performance models capable of adapting to real-world situations!
The fundamentals of overfitting
What isoverfitting ?
Overfitting is a common phenomenon in machine learning. It occurs when a model learns the peculiarities of training data too well, to the point of losing its ability to generalize on new data (see [1]). In other words, the model becomes too specialized in capturing the "eccentricities" and noise present in the training dataset (see [2]).
To better understand this concept, let's imagine a chef learning a new recipe. The chef - who represents overfitting in our example - meticulously memorizes every detail of the recipe, including precise measurements and steps. He can reproduce the dish exactly as written, but has difficulty adapting to slight variations or unexpected ingredients (see [2]).
Why is overfitting a problem?
Overtraining is problematic because it compromises the model's ability to perform well on new data. An over-tuned model has a very high success rate on training data, up to 100%, but at the expense of its actual overall performance. When these models are deployed in production, they can run into difficulties if the actual results don't match expectations, which is a sign of overfitting.
This phenomenon can result from a mismatch between model complexity and dataset sizing. Common causes include :
- Low volume of training data
- A large amount of irrelevant information in the dataset
- Training based on data sampling only
- An overly complex model (see [3])
Concrete example(s) of overfitting
To illustrate overfitting, let's take the example of a model estimating a man's average height as a function of age. An over-trained model, trained with average height by age, could predict that the same 13-year-old would measure 165 cm, then 162.5 cm at 14, and 168 cm at 15 - based on averages. This detailed curve has no scientific basis and too closely reproduces each training sample that was given to the model rather than drawing generalized trends.
To detect overfitting, we usually compare the model's performance on the training set and on a separate test set. A model whose performance is significantly lower on the test set will almost certainly have been overlearned.
Strategies for reducing overfitting
To combat overfitting, data professionals have a rather effective arsenal of techniques at their disposal. These strategies aim to improve the generalization capacity of machine learning models.
To industrialize these overfitting reduction strategies, it is important to integrate solutions such as Saagie into machine learning projects to optimize model lifecycle management and anticipate these problems.
Reducing neural network complexity
Simplicity is often the key to avoiding overfitting. A less complex model is less likely to overfit the training data. This can be achieved by :
- Carefully select the most relevant features, eliminating those that don't add significant value.
- Reducing the number of layers and neurons in neural networks.
- Choose simpler models suitable for most applications.
Regulation techniques
Regularization methods play an important role in reducing overall model complexity. They enable a balance to be struck between performance and generalization. These techniques include :
- L1(Lasso) and L2(Ridge) regularization, which penalizes coefficients that are too high.
- Dropout for neural networks, which consists in randomly ignoring certain units during training.
- Early stopping, which interrupts training when performance on the validation set begins to deteriorate.
Data expansion and diversification
Increasing the size and diversity of the dataset is a powerful strategy for combating overfitting. Here's how to do it:
- Collect more real data where possible.
- Usingdata enhancement to create realistic synthetic variations:
- For images: rotate, crop, modify brightness.
- For text: paraphrase, word replacement.
- For audio: speed change, tone variation.
- For tabular data: disruption of numerical values, one-hot encoding.
These strategies, combined with other techniques such as cross-validation and hyperparameter optimization, enable us to create more robust, better-performing models on new data.
Assessing and monitoring overfitting
Evaluation and monitoring of overfitting are essential to guarantee the performance and generalizability of machine learning models. These processes ensure that the model performs satisfactorily under real-world conditions and is capable of generalizing beyond the training data.
Validation methods
Cross-validation is an advanced technique widely used to evaluate machine learning models. It involves dividing the data into k subsets, or folds. The model is then trained k times, each time using k-1 subsets for training and a different subset for validation. This approach provides a more robust estimate of model performance.
Stratified cross-validation is a particularly useful variant for unbalanced datasets. It ensures that each set contains approximately the same proportion of each class as the complete dataset.
Another commonly used method is to divide the data into training and test sets. With this simple approach, one part of the data is used to train the model, while the other is used to analyze its performance.
Performance metrics
To quantify the performance of a model, various metrics are used, depending on the type of task (classification, regression, etc.). Common metrics include precision, recall, F1 score and mean square error.
The confusion matrix is also a valuable tool for assessing the performance of classification models. It allows you to visualize true positives, true negatives, false positives and false negatives, providing an overview of model accuracy.
Visualization tools
Learning curves are powerful visual tools for analyzing model performance. They plot model performance as a function of training set size, helping to understand how adding data affects performance.
To detect overfitting, it is also very important to compare theloss between training and validation data. When overfitting occurs, the loss increases, and the loss of the validation data becomes significantly greater than that of the training data.
By monitoring these metrics and using these visualization tools, data scientists can identify and correct overfitting, ensuring that their models are robust and perform well on new data.
Conclusion
Overfitting represents a major challenge in the field of machine learning, with a considerable influence on model reliability and performance. This article has explored the fundamental aspects of overfitting, presented effective strategies for reducing it, and examined methods for evaluating and monitoring it. Understanding this phenomenon and applying appropriate techniques are essential for creating robust, high-performance models.
Ultimately, the fight against overfitting is an ongoing process that requires a balanced approach. By combining strategies such as reducing model complexity, regularization techniques and data augmentation, you can significantly improve the generalizability of your models. Constant monitoring and the use of appropriate evaluation tools will ensure that models remain efficient and reliable in real-world situations.
References
[1] - https://www.actuia.com/faq/quest-ce-que-le-surapprentissage/
[2] - https://www.picsellia.fr/post/comprendre-overfitting-machine-learning
[3] - https://blog.fandis.com/fr/sci-fa-fr/quest-ce-que-le-surapprentissage-dans-lapprentissage-automatique/
[4] - https://blent.ai/blog/a/surapprentissage-comment-eviter
[5] - https://larevueia.fr/7-methodes-pour-eviter-loverfitting/
[6] - https://www.innovatiana.com/post/data-augmentation-for-ai
[7] - https://www.innovatiana.com/post/how-to-evaluate-ai-models
[8] - https://www.saagie.com/fr/blog/machine-learning-comment-evaluer-vos-modeles-analyses-et-metriques/
