AI revolutionizes every industry it hits. However, successfully developing and implementing it in a business is no walk in the park. Developing a high-performing AI model involves various challenges, which, if not managed properly, can lead to project failure.
Overfitting is one of the most common reasons for AI project failure, can lead to erroneous AI modes, and poses a significant barrier to successful AI development. This article explores:
- What AI overfitting is,
- How to detect it,
- And 4 best practices for avoiding it
What is AI-overfitting?
The purpose of an AI/ML model is to make predictions from real-life data after being trained by the training dataset (see Figure 1). Overfitting occurs when a model is too attuned or overly fitted to the training data and does not function accurately when it is fed with any other data.
In other words, The AI/ML model memorizes the training data rather than learning from it. An overfitted model will present accurate results while working with the training dataset and present erroneous results working with an unseen or new dataset.
Figure 1. Simple illustration of an overfitted AI/ML model.
How to detect overfitting?
Detecting overfitting in an AI/ML model comes down to evaluating its accuracy. One of the most popular methods of testing the accuracy of an AI/ML model is K-fold cross-validation. In this method, the dataset is split into k number of subsets, and the same number (k number) is the number of evaluations performed. One of those subsets is appointed as the validation/test set, and the rest are training sets.
The model goes through each evaluation round, in which the validation set changes until all of the subsets have once acted as the validation set. The average of the values from these iterations presents the model’s overall performance (see figure 2 as an example).
Ultimately, if the difference between the validation set and the training set is significant, the model is overfitted.
Figure 2. Illustration of a k-fold cross-validation method with 5 folds/evaluations.
How to avoid overfitting?
This section highlights 4 commonly used techniques to avoid overfitting in an AI/ML model:
1. Expand the dataset
Training the model with more data is one of the most effective ways of avoiding overfitting. Having a larger dataset will expand the range of the model’s capabilities and cover all possible outcomes that can happen after the deployment stage.
This technique is executed at the beginning of the AI development process: data collection/harvesting. Check out this comprehensive article to learn more about the best practices of data collection.
You can also check our data-driven lists of data collection/harvesting services.
2. Achieve optimum level by stopping early
In this method, the training is stopped at a point before the model starts learning the noise in the dataset and after the model has been sufficiently trained. The optimum point occurs when the performance of the model on the validation dataset starts to reduce after every evaluation (accuracy starts to decrease, and loss starts to rise). At this point, the training should be stopped.
However, stopping the training too early can also risk another issue which is the opposite of overfitting: underfitting (See figure 3). Best way to avoid it is to continuously monitoring and model retraining as a part of Continuous Training (CT) in MLOps practices.
Figure 3. The optimum point to stop the training.
3. Data augmentation
When collecting more data is not an option, data augmentation can be used to create more data from the existing set. This can be done by creating different variations of the existing data and multiplying them (see Figure 4).
While data augmentation can improve the model’s accuracy, reduce data collection costs, and enhance model stability, it’s better to use caution since it can also increase the noise in the model.
For more in-depth knowledge on data augmentation, check out these comprehensive articles:
Figure 4. Image data augmentation examples
4. Model simplification
Simplifying the model can also help reduce overfitting. For example, if a model is being developed to make predictions, specific parameters are set to be used to make those predictions. If those parameters are too complicated and have unnecessary elements, the model can become erroneous.
Through feature selection, the training data that covers the most critical parameters/features are selected, and the irrelevant data is removed to simplify the dataset and reduce overfitting.
In simpler words, making the model and the training data smaller and more relevant (based on your own project) can make it less overfitting.
Further reading
- 3 Ways to Apply a Data-Centric Approach to AI Development
- Sentiment Analysis: How it Works & Best Practices
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