Overfitting in Machine Learning: solutions and tips
Overfitting is a major challenge in the field of machine learning. This phenomenon occurs when a model learns training data too well, to the point where it loses its ability to generalize to 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 Fundamental Aspects of the concept of overfitting and present effective strategies to reduce it. It also looks at methods for evaluating and monitoring this phenomenon throughout the process of developing artificial intelligence models. Through this article, you will be able to learn the basics to create more robust and more efficient models, capable of adapting to real world situations!
The Fundamentals of Overfitting
What is overfitting?
Overfitting is a common occurrence in machine learning. It occurs when a model learns the particularities of training data too well, to the point where it loses its ability to generalize to new data (see [1]). In other words, the model becomes too specialized in capturing the “eccentricities” and noise present in the training data set (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 remembers every detail of the recipe, including measurements and precise steps. He can replicate the dish exactly as it is written, but has trouble adapting to slight variations or unexpected ingredients (see [2]).
Why is overfitting a problem?
Over-learning is a problem because it compromises the ability of the model to work well on new data. An overadjusted model has a very high success rate on training data, reaching up to 100%, but at the expense of its overall real 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 may result from a lack of match between the complexity of the model and the sizing of the data set. Common causes include:
- A low volume of training data
- A large amount of irrelevant information in the data set
- Training Focused Only on Data Sampling
- A model that is too complex (see [3])
Concrete example (s) of overfitting
To illustrate overfitting, let's take the example of a model estimating the average height of a man based on his age. An overadjusted model, trained with the average height by age, could predict that the same 13-year-old teenager measures 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 accurately reproduces each training sample that was given to the model rather than drawing generalized trends.
To detect overfitting, the performance of the model is generally compared on the training set and on a separate test set. A model whose performance is much lower on the test game will most certainly have been overtrained.
Strategies to reduce overfitting
To combat overfitting, data professionals have an arsenal of fairly effective techniques at their disposal. These strategies aim to improve the generalization capacity of machine learning models.
To industrialize these strategies to reduce overfitting, it is important to integrate solutions such as Saagie As part of 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 adapt excessively to the training data. To achieve this, we can:
- Select the most relevant characteristics wisely, eliminating those that do not provide significant value.
- Reducing the number of layers and neurons in Neural networks.
- Choose simpler models that are suitable for the majority of applications.
Regularization techniques
Regularization methods play an important role in reducing the overall complexity of the model. They make it possible to find a balance between performance and generalization. These techniques include:
- L1 regularization (Lasso) and L2 (Ridge) which penalize coefficients that are too high.
- The Dropouts for neural networks, which consists in randomly ignoring certain units during training.
- Early Stopping (Early Stopping) Which stops the training when the performance on the validation set starts to deteriorate.
Data growth and diversification
Increasing the size and diversity of the data set is a powerful strategy to combat overfitting. Here's how to do it:
- Collect more real data when possible.
- Use techniques ofData Increase To create realistic synthetic variations:
- For images: rotating, cropping, changing the brightness.
- For text: paraphrase, word replacement.
- For audio: speed change, tone variation.
- For tabular data: disturbance of numerical values, encoding One-Hot.
These strategies, combined with other techniques such as cross-validation and hyperparameter optimization, make it possible to create more robust and efficient models on new data.
Overfitting Assessment and Monitoring
Evaluating and monitoring overfitting is essential to ensure the performance and generalization of machine learning models. These processes ensure that the model works satisfactorily under real conditions and that it is capable of generalizing beyond training data.
Validation methods
Cross-validation is an advanced technique that is widely used to assess machine learning models. It consists in dividing the data into k subsets, or more. The model is then trained k times, each time using k-1 subsets for training and a different subset for validation. This approach makes it possible to obtain a more robust estimate of the performance of the model.
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 full data set.
Another commonly used method is the division of data into training and test sets. This simple approach allows one part of the data to be used to train the model and the other 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 accuracy, recall, F1 score, and mean squared error.
Confusion matrix is also a valuable tool for evaluating the performance of classification models. It allows you to visualize true positives, true negatives, false positives, and false negatives, thus providing an overview of the accuracy of the model.
Visualization tools
Learning curves are powerful visual tools for analyzing model performance. They allow the performance of the model to be plotted against the size of the training set, helping to understand how the addition of data affects performance.
To detect overfitting, it is also very important to compare the loss (Loss) between training data and validation data. When overtraining occurs, the loss increases and the loss of validation data becomes significantly greater than the loss of 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 efficient on new data.
Conclusion
Overfitting represents a major challenge in the field of machine learning, with a considerable influence on the reliability and performance of models. This article explored the fundamental aspects of overlearning, presented effective strategies for reducing it, and examined methods for evaluating and monitoring it. Understanding this phenomenon and applying appropriate techniques are essential to create robust and efficient models.
At the end of the day, fighting overfitting is an ongoing process that requires a balanced approach. By combining strategies such as reducing model complexity, regularization techniques, and increasing data, you can significantly improve the generalizability of your models. Constant monitoring and the use of appropriate assessment tools will allow you to ensure that the models remain efficient and reliable in real world situations.
References
[1] - 🔗https://www.ibm.com/think/topics/overfitting
[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/
