Which technique is often used to avoid overfitting in machine learning?

Applying dropout in neural networks is a widely used technique to avoid overfitting. Dropout works by randomly setting a fraction of the input units to zero during training. This prevents the model from becoming overly reliant on any individual neuron, thus encouraging the network to learn more robust features that are more generalizable to unseen data. By forcing the network to learn multiple independent representations, dropout diminishes the risk of memorizing the training data, resulting in a model that performs better on new, unseen examples.

In contrast, other methods like increasing the training set size, while beneficial for enhancing the generalization of a model, do not inherently impose the same structural constraints on the learning process as dropout does. Similarly, reducing the number of features can help streamline the model, but may not address the complex interactions that lead to overfitting. Utilizing unsupervised learning methods differs fundamentally in approach and often focuses on finding patterns in data without labeled outcomes, which may not directly relate to the challenge of overfitting in supervised learning contexts. Thus, dropout specifically addresses overfitting in the way that other methods might not, reinforcing its significance in neural network training.