Deep neural networks trained on data with mislabeled examples demonstrate a distinct learning trajectory: they initially fit the cleanly labeled examples and only later begin to interpolate the mislabeled ones. By terminating the training process after the clean data is learned but prior to the memorization of random labels, practitioners can effectively guarantee better generalization.

Learning Dynamics under Label Noise

Label noise occurs when a training dataset contains incorrectly or randomly assigned labels, introducing intrinsic variability into the learning process. For example, when predicting mortality among patients, the true outcome may be ambiguous or recorded incorrectly. While neural networks can eventually interpolate these noisy labels, their presence makes regularization techniques like early stopping crucial to prevent overfitting.

Claude

In supervised learning problems, a label, also known as a target, is a special attribute of an example that a model aims to predict. It represents the known outcome or ground truth that is not part of the model's input features.

Machine Learning Label

Dive into Deep Learning

Based on the supervised learning scenario described below, identify the outcome variable (also known as the target or dependent variable) and briefly explain why it is classified as such.

Identifying the Outcome Variable in a Predictive Model

A data scientist is developing a supervised learning model to determine whether an email is 'spam' or 'not spam'. The model is trained on a dataset of thousands of emails, each with a corresponding label. In this scenario, what does the outcome vector, denoted as Y, consist of?

A real estate company is building a statistical model to predict the selling price of a house based on its features, such as square footage, number of bedrooms, and location. In the context of a supervised learning problem, what specific quantity represents the outcome value (denoted as $y_i$) for a single house in their dataset?

Learn Before

Related

Learn After