Understanding What is Regularization in Machine Learning
In machine learning, the pursuit of models that generalise well to unseen (new) data is paramount. However, achieving this balance between complexity and generalisation can be challenging. Regularisation techniques offer a solution by imposing constraints on model parameters during training, effectively mitigating overfitting and enhancing the model’s ability to generalise. So what is regularization?
Regularization
Let’s dive into the concept of regularisation, its importance, and popular techniques employed in the field of machine learning. For better comprehension, we simply the concepts as follows:
- Overfitting occurs when a model learns the training data too well, capturing noise and irrelevant patterns that do not generalise to unseen (new) data.
- Regularisation (in machine learning) refers to a set of techniques used to prevent overfitting and improve the generalisation performance of machine learning models.
- Regularisation methods introduce additional constraints or penalties on the model parameters during training to discourage overly complex models and promote simpler solutions that generalize better.
The main goal of regularization is to balance fitting the training data well and avoiding excessive complexity. A model that is too complex may perform well on the training data but fail to generalise to new, unseen data. Regularisation helps prevent this by penalising complexity, encouraging the model to learn only the most important patterns in the data.
Below is a simple example of a balanced vs overfitted model.
Overfitted model
Balanced model
Regularization Techniques
In this section, we cover some of the most popular regularisation techniques. These ML regularisation techniques are essential tools for developing models that generalise well to diverse datasets and real-world scenarios. Let’s explore them.
L1 and L2 Regularisation
L1 and L2 regularisation, also known as Lasso and Ridge regression, respectively, introduce penalty terms to the loss function during training. Firstly, L1 regularisation penalises the absolute values of the model’s coefficients, promoting sparsity and feature selection. On the other hand, L2 regularisation penalises the squared magnitudes of the coefficients, effectively shrinking them towards zero. Both techniques help prevent overfitting by limiting the magnitude of model parameters.
Dropout
Dropout is a regularisation technique commonly used in neural networks. During training, random units in the network are temporarily “dropped out” or set to zero with a certain probability. This process introduces noise into the network, forcing it to learn more robust features and reducing reliance on specific neurons. Dropout effectively acts as an ensemble method, training multiple subnetworks simultaneously and averaging their predictions during inference.
Early Stopping
Early stopping is a simple yet effective regularisation technique that prevents overfitting. It does so by monitoring the model’s performance on a validation set during training. Training is halted when the validation error starts to increase. This in turn indicates that the model is beginning to overfit the training data. By stopping the training process early, early stopping ensures that the model generalizes well to unseen data without excessively fitting the training set.
Data Augmentation
Data augmentation is a regularisation technique commonly used in computer vision tasks. By applying transformations such as rotation, scaling, and flipping to the training data, data augmentation effectively increases the diversity of the training set. This augmentation introduces variability into the training process, making the model more robust to variations in the input data and reducing the risk of overfitting.
Elastic Net Regularisation
Elastic Net regularisation combines both L1 and L2 regularisation by adding penalties from both techniques to the loss function. It balances between L1 and L2 regularisation, aiming to capture the benefits of both techniques.
Cross-Validation
Cross-validation is a technique used to assess the generalisation performance of a model. It does so by splitting the data into multiple subsets (folds), training the model on some folds, and evaluating it on others. This helps provide a more reliable estimate of the model’s performance and can help prevent overfitting by averaging results across different subsets of the data.
The Bottom Line
Regularization techniques play a vital role in the field of machine learning by addressing the fundamental challenge of overfitting. It imposes constraints on model complexity and encouraging simpler solutions. As such, it helps strike a balance between fitting the training data and generalising to new, unseen data. Understanding and effectively applying regularisation techniques is essential for developing models that exhibit robust performance across diverse datasets and real-world scenarios.
