How to Avoid Overfitting and Underfitting in Machine Learning Models?

sdvsdvsdv1@ / 30 Nov, 2024

Therefore, one of the most essential problems of machine learning is how to avoid underfitting and overfitting. These two terms refer to some of the problems that arises during the training of a model and affects a model performance on new data. In this blog, let’s take a closer look at what overfitting and underfitting means and examine several tips that can help you prevent these problem in your machine learning models.

What is Underfitting?

When a model is too simple to capture all the training data then it is said to be an underfitting model. In general, it is not capable of learning the intricacies of the data and therefore does not do well for the data set used for training or any other data set (test data). Underfitting often happens when:

• The model is not adequate to the richness of the data set and the framework under consideration.

• On the Internet, the model is trained for too few epochs or iterations.

• The features used for training of the model are either uninformative or don’t have enough dimensions in order to describe the structure of data.

Signs of Underfitting:

• Low training accuracy: The results of the model are rather poor even on the training data.

• Low test accuracy: It also shows low generalization capability on unseen data and on the new data specifically.

1. Faster Training Time

• Simpler Models: Underfitting is more likely to occur when using less complex methods (linear regression, decision trees with low depth, etc) , which are, generally, trained faster than, for example, deep neural networks or large ensembles.

• Less Computational Cost: Indeed, models with fewer parameters are less computationally intensive and hence are beneficial when handling large databases or where computing power is of essence (for instance in RAM–intensive computation).

Benefit: If there are no complex patterns in the task then underfitting can be helpful in terms of with rapid experimentation and placing the model into production without significant loss in its performance.

2. Generalization Improve in the Cases of Low Complexity

Avoiding Overfitting in Simple Data: Indeed, there are situations where the data are already comparatively ‘simple,’ and extraordinary elaborate models are not required. For instance in cases where the situation between the features of concerns and the target is linear and easily separable using hyper plane such as the linear regression the use of the model with higher non-linearity than the actual data will only do more harm than good.

• Robustness to Noise: An underfitting model, in general, means that the model is not very sensitive to the noise involved in the data. Although it may not be as detailed as the dataset it has analyzed it is also more resistant to overfitting on outliers or other non-real patterns, which makes it usable in some cases.

Benefit: Sometimes an underfitting model will generalize better because the extra noise and low level irrelevant details are to much for the model to handle.

3. Simpler Interpretation

• Easier to Understand: Underfitting models are tend to be less complex (linear models or decision tree with not many splits). They are easy to explain, which is important in disciplines where, for example: data needs to be explained to patients, regulators, shareholders, or investors.

• Less Risk of Overfitting to Unimportant Features: This is say that in simple model, irrelevant features or noise in the data set may be easily ignored hence few complications in model explainability.

Benefit: If interpretability of the model is the priority (for example when the application of the model is regulated) then it is better to use a slightly underfitting model, which is easy to explain rather than a complex one, which is hard to comprehend.

4. Good for Baseline Models

• Establishing a Benchmark: Underfitting can be helpful in terms of creating a benchmark at the specification level of a more complex model, for instance, when applying a highly parameterized statistical apparatus. When a given style or model underfits, developing a radically simpler model or approach can be used as a benchmark to determine exactly how much better a more complex style/model is.

• Quick Prototyping: If the goal is to create a proof of concept of a machine learning pipeline in order to identify and exclude problematic cases as soon as possible, simpler models can be used for this purpose in order to avoid having to recalibrate the pipeline every time.

Benefit: It first serves as a preliminary to determine whether more complex modeling methods are needed at all due to underfitting.

5. Predictive Stability in Sparse Data

Stability in sparse data is as unpredictable as predicting the stability of a shaky table.

• Handling Small Datasets: Indeed, underfitting could be optimal in conditions when it is preferable to overfit on available data. If there are only a few data points, a exotic model can easily end up overfitting it, on the other hand a simple model which underfits the data will still have more stable predictions in new data.

• Reduced Risk of Overfitting on Small Datasets: Indeed, the practice of fitting noise is more frequent when models are selected that have higher order and when the number of observations is relatively small. An overly simplistic model doesn’t create too many learned patterns and features, so it can’t overfit as easily.

Benefit: In low-data environment, underfitting could even perform better overall than overfitting due to overtraining of models from small amounts of data.

What is Overfitting?

This happens when the model chosen is far too specialized to capture data patterns but picks up also noise and random variations. Therefore, the model does very well with the training data, but poorly with any new or unknown data. This is usually as a result of the model complicated nature which makes it to learn the training data very well but struggle to recognize new data.

Signs of Overfitting:

High training accuracy but low test accuracy: The model is actually just memorizing the training data and this is not good learning of generalizable patterns.

Overly complex model: It could also be that the model has many of parameters compared to the size of the training and evaluation set or that the model is deep.

Benefits of Overfitting:

In general, overfitting is viewed as an issue when performing machine learning since high levels of fit results in bad generalization to unseen data, yet there are several cases where having a high level of fit might be beneficial. Knowing these advantages will go a long way in helping you decide when overfitting might be allowed – or even desirable – before you attempt to address it using tools such as regularization, cross-validation, or model pruning. Here are some potential benefits of overfitting.

1. Optimal Fit to Training Data

• Maximized Performance on Training Data: It is useful when enriching the details of the training data set is the main agenda and gaining high accuracy on the given data set has to be achieved. In some cases, overfitting can be useful, on the condition that the dataset is modest, simple, and behaves extremely well. For instance, if you want a model that has high accuracy on the known dataset perhaps for a controlled experiment, overfitting guarantees the model does well on the data set in question.

• Benefit: In cases where optimization on the test data is not a significant issue, for example, when data is fully controlled or you are using artificial data that you know what patterns to look out for, then overfitting could cause better results to appear on the training data set giving one a very clear picture of how the gone model is going to perform.

2. Capturing Complex Patterns in the Data

• Learning Complex Data Distributions: Overfitting enables a model to learn very many features in the data set including noise and outliers. In some cases, there exists complex relationship in the attribute values which a simple model (over-simplified or underfitting) cannot comprehend.

• Benefit: And in highly complex data that simple model might indeed miss out some of these hidden, subtle patterns that overfitting does uncover. This may come handy in scenarios where detailed analysis of one’s data is needed, and the process must involve the exploration of the space of all possible dependencies between characteristics/features.

3. Model Exploration in Research

• Hypothesis Generation: When it comes to the exploratory data analysis or exploratory research, perhaps overfitting can be useful to give hints for subsequent studies. If you’re getting familiar with a new data set and the aim is to learn an equation relating different variables to the all-important Y, an overfit model is likely to show new interactions, new correlations, new trends which can serve as starting hypotheses for a new study.

• Benefit: Often overfitting can be used as a kind of a data mining technique whereby researchers seek to identify structures which may not strictly be discernible and may later prove to be more rigorous.

4. Improving Performance on Specific Subsets of Data

• Modeling Specific Subgroups: Occasionally, it is desirable for data to over fit some of the time, especially when developing a model is required to perform exceptionally for a specific scope of data. For instance, in segmentation issues where the goal is to find such specific detail about a given group, then over fitting could help to achieve a very particular model which might be very efficient within that area despite being ineff ective on a broader perspective.

• Benefit: Overfitting might give you high accuracy and a very good precision, for the very specific segment of data that you are concerned with; in the above-mentioned marketing model customers’ segment could equate to overfitting.

5. Maximizing Accuracy for Predictive Tasks with Small Datasets

• Limited Data Availability: In this case of working with a limited amount of data, it is possible that even an overfitting model will provide a better level of accuracy than a simple model. Thus for small datasets, overfitting, can at times, enhance the capability of the model to fit the data more vividly, since the training set is not very large, patterns of such a size could easily be learned with lot of noise.

• Benefit: Occasionally, for small datasets, it may lead to better prediction of data, particularly if the dataset is free from noise, and the model complex on the same data.

Strategies to Avoid Overfitting and Underfitting

1. Use the Right Model Complexity

• For Underfitting: Instead select a model that it is capable of learning more complex relations in comparison to the given model. For example, use a function such as linear regression while the data is nonlinear using a polynomial regression instead of the linear regression function.

• For Overfitting: If the data input is not very complex, it is better to use a model with fewer parameters to avoid cases where the model attempts to approximate complex datasets which sums up to low capacity. For example it is possible to use simpler decision tree containing small number of nodes to a certain depth or linear model if the dataset is rather small and contains rather simple relations.

2. Increase Training Data

• It also showed that more training data is beneficial to model in avoiding overfitting since it focuses on the general features without giving much attention to the noise. This can help mean that we do not overfit our data nor even underfit; the model gets a better representation of the real distribution.

• Underfitting: You may not have enough data to feed to an ML model, let alone train it to a more advanced level. To this end, enlarging the dataset can be of useful.

• Overfitting: A bigger training set allow the model to learn better due to the variation seen by the model that prevents the model from building a custom that matches the training set.

3. Cross-Validation

• In cross validation the full set of data is divided and the user uses some of it to train the model and tests it on the rest. This enables you to evaluate the model on complexity not witnessed in the training set enabling you to rate the model’s ability to generalize on unseen data.

• Underfitting: If the model is doing really bad in all of the cross-validation folds then one has to make the model more complex.

• Overfitting: If the model gives good accracy on few folds and bad on others then it indicates that the model is memorizing the particular fold data.

4. Regularization

The strategies of model’s regularization are useful for avoiding the formation of overfitting by penalizing the large amount of model’s complexity. Two common regularization methods are:

• L1 Regularization (Lasso): This introduces a penalty proportional to the absolute value of the coefficients of the model which in effect compresses the model by making certain of the coefficients equal to zero.

• L2 Regularization (Ridge): This sums up a quadratic penalty for the coefficients meaning that they decrease in size thus reducing sensitivity of the model to the training inputs data set.

Basically, the regularization methods assist in model complexity reduction as well as enable the model understand important tendencies in the data.

5. Early Stopping

• In training process, early stopping is the practice of stopping the training process before the model gets too much closer to the training data as in the case of iterative algorithm like gradient descent. To do this is give a small subset known as the validation set data to the model to check when its performance on the validation set begins to decline, this is the sign that the training is too much.

• Underfitting: If the stop here, the model might not have learnt enough, a problem known as underfitting.

• Overfitting: Cross-validation prevents overfitting as well because the model ends before it starts fitting to the training data.

Conclusion

Overfitting and underfitting are two sides of the same coin, which needs great efforts to manage with sufficient complication, size of data, time of training and correct technique. In this way, we are able to front the roots of these problems and generate machine learning models that generalize well to unseen data and perform reasonably well. These and other similar principles are prerequisite to building good machine learning applications in simple linear regression model or deep learning architecture.

By taking time to eliminate both underfitting and overfitting at Softronix, you get to be more certain that the models no longer merely memorize the data but learn from it; thus, making your machine learning endeavors even more significant with our help at Softronix.