How to Avoid Overfitting in Machine Learning?

Last Updated : 06 Jan, 2024

Overfitting in machine learning occurs when a model learns the training data too well. In this article, we explore the consequences, causes, and preventive measures for overfitting, aiming to equip practitioners with strategies to enhance the robustness and reliability of their machine-learning models.

What is Overfitting?

Overfitting can be defined as a phenomenon where a machine learning model learns the training data too well, capturing not only the underlying patterns but also the noise and fluctuations present in that particular dataset. This results in a lack of generalization ability when confronted with new, previously unseen data. The balance of bias and variance is crucial in machine learning and model development. Understanding this tradeoff is essential for creating models that generalize well to new, previously unknown data. Let us look at the terms bias and variance and how they interact.

What is Overfitting?

1. Bias:

Bias is the error introduced by using a simplified model to approximate a real-world problem. A high bias indicates that the model is overly simple and incapable of capturing the data's underlying patterns.
High-bias models are prone to systematic errors, consistently underestimating or overestimating true values. Underfitting happens when a model performs poorly on both training and testing data.
A linear regression model applied to a dataset with a complex non-linear relationship, for example, would be biased because it cannot capture the non-linear patterns.

2. Variance:

Variance is the sensitivity of the model to fluctuations or noise in the training data. High variance indicates that the model is overly complex and captures not only the underlying patterns but also the noise in the data.
High-variance models are sensitive to small changes in the training data, resulting in good training set performance but poor generalization to new data. This is known as overfitting.
For example, when applied to a small dataset, a highly flexible decision tree with a deep structure may exhibit high variance, capturing noise rather than true underlying patterns.

Example:

The concept of the overfitting can be understood by the below graph of the linear regression output:

overfitting-and-underfitting — OVERFITTED MODEL

The graph above illustrates how the model attempts to account for every data point in the scatter plot. Although it appears effective, that is not the case in practice. The regression model will produce prediction errors because its objective is to identify the best fit line, and since there isn't one here.

What can be the consequences of overfitting?

Overfitting has a significant impact on a model's dependability and performance in machine learning. Here are the key consequences:

Improper generalisation is the main effect of overfitting. Even though the model performs incredibly well on the training set, it is unable to accurately generalize to new, untested data. This limits its usefulness in real-world applications.
On new data, overfit models frequently exhibit reduced predictive power. They may make overly specific predictions based on inconsistencies in the training set, resulting in inaccurate results when faced with different data distributions.
Models that are overfitted are less resilient and more susceptible to changes in the input data. The predictions of the model can fluctuate significantly, even with small changes or noise in the data.
When a model is overfitted, it may memorize the training data rather than learning general patterns. This memorization is harmful when the model encounters new instances that differ from the specific examples in the training set.
Depending on the problem, overfit models may have an increased risk of false positives or false negatives. This can be especially troublesome in applications where precision and dependability are critical.
An overfit model might not work as well on all datasets or in real-world situations. It may be less adaptable if its performance is limited to the particular circumstances found in the training set.
It can be computationally costly and inefficient to train extremely complex models that overfit the data. Rather than concentrating on the key patterns in the data, resources are used to learn noise and unimportant details.
Overfitting frequently results in overly intricate models with a lot of parameters. This kind of intricacy can make it harder to understand and update the model.
Debugging overfit models can be difficult because the source of poor performance may be deeply embedded in the model's noise fitting rather than the true underlying patterns.

Why Does Overfitting Occur?

Overfitting occurs in machine learning for a variety of reasons, most arising from the interaction of model complexity, data properties, and the learning process. Some significant components that lead to overfitting are as follows:

Model Complexity: When a model is selected that is too complex for the available dataset, overfitting frequently occurs. Overfitting of the training data can result in models with many parameters or high flexibility that capture noise and fluctuations rather than true underlying patterns.
Inadequate Data: When the training dataset is small, models may struggle to learn the actual patterns that exist. With fewer examples, there is a greater chance that the model will memorize the training data rather than generalizing to new, previously unseen instances.
Noisy Data: The model may incorporate noise, outliers, and irrelevant information into its learning process if the training data contains any of these things. This may result in fitting the noise rather than the data's true underlying relationships.
Lack of Regularization: Models may lack complexity constraints if appropriate regularization techniques are not used. Regularization methods like L1 and L2 regularization help to prevent overfitting by penalizing overly complex models.
Overfitting to Outliers: If a model is sensitive to outliers in the training data, it may be overfit to these outliers, resulting in poor generalization to new data that does not contain those outliers.
Memorization vs. Generalization: Some models can memorize training data, particularly if they are highly flexible. This memorization can be harmful when the model encounters new data that differs from the training set.
Feature Engineering: Improper handling of features or the inclusion of irrelevant features can contribute to overfitting. Effective generalization of models is largely dependent on feature engineering and feature selection.

Methods to Avoid Overfitting

To avoid overfitting in machine learning, you can use a combination of techniques and best practices. Here is a list of key preventive measures:

Cross-Validation: Cross-validation involves splitting your dataset into multiple folds, training the model on different subsets, and evaluating its performance on the remaining data. This ensures that your model generalises well across different data splits. For example, in k-fold cross-validation, you divide your data into k subsets. You train and validate your model k times, using a different fold as the validation set and the remaining folds as the training set each time.
Split Your Data: For training, validation, and testing, divide your data into distinct subsets. This ensures that your model is trained on one subset, hyperparameters are tuned on another, and performance is evaluated on a completely separate set. For example, you could use an 80/10/10 split, with 80% of the data going to training, 10% going to validation, and 10% going to testing.
Regularization: Regularization techniques add penalty terms to the loss function to prevent the model from fitting the training data too closely. For example, in linear regression, L1 regularization (Lasso) adds the absolute values of the coefficients to the loss function, encouraging some coefficients to become exactly zero. L2 regularization (Ridge) augments the loss function with the squared coefficient values.
Data Augmentation: Data augmentation is the process of creating new samples by applying random transformations to your training data. For example, during image classification training, you could randomly rotate, flip, or zoom into images to generate variations of the original images.
Feature Selection: To reduce the risk of overfitting, select the most relevant features and exclude irrelevant or redundant ones.
Example: Using techniques such as Recursive Feature Elimination, you iteratively remove the least important features until the desired number is reached.
Ensemble Learning: Ensemble methods combine predictions from different models to improve overall performance and reduce overfitting. Random Forest is an ensemble method that builds multiple decision trees and combines their predictions. Each tree is trained on a different subset of the data.
Early Stopping: During training, monitor the model's performance on a validation set and stop when performance begins to degrade. For example, in neural network training, you might stop training if the validation loss does not improve after a certain number of consecutive epochs.
Dropout: Dropout deactivates a subset of neurons at random during training to avoid over-reliance on specific neurons. Example: In a neural network, the network is trained on the remaining active neurons, while random neurons are set to zero during each training iteration.
Reduce Model Complexity: To avoid overfitting, select a simpler model architecture. Example: Take into consideration using a simpler architecture with fewer layers or nodes in place of a deep neural network with many layers.
Increase Training Data: Gather more information to help the model better grasp the underlying patterns in the data. Example: A larger dataset containing a variety of positive and negative sentiment examples can improve the model's ability to generalize in a sentiment analysis task.

Conclusion

Overfitting must be avoided if machine-learning models are to be robust and reliable. Practitioners can improve a model's generalisation capabilities by implementing preventive measures such as cross-validation, regularisation, data augmentation, and feature selection. Ensemble learning, early stopping, and dropout are additional techniques that help to build models that balance complexity and performance. Selecting an appropriate model architecture, increasing training data, and adhering to best practices in data splitting are additional keys to overcoming overfitting challenges. With these precautions, machine learning practitioners can ensure that their models generalise well to diverse datasets and real-world scenarios, fostering predictability and accuracy. Continued research and application of these strategies align with the ongoing pursuit of optimising machine learning practices.

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Practice Tags :

How to Avoid Overfitting in Machine Learning?

What is Overfitting?

1. Bias:

2. Variance:

What can be the consequences of overfitting?

Why Does Overfitting Occur?

Methods to Avoid Overfitting

Conclusion

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