In supervised machine learning , the “answer” or “result” portion of an example . Each labeled example consists of one or more features and a label. For example, in a spam detection dataset, the label would probably be either “spam” or “not spam.” In a rainfall dataset, the label might be the amount of rain that fell during a certain period.1
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See also
Proxy label : Data used to approximate labels not directly available in a dataset.
Footnotes
목차 관련 글 Minority class (machine learning) 이 글을 인용: “The less common label in aclass-imbalanced dataset.” 역전파 이 글을 인용: “The difference between the prediction and the label value is the loss for that example.” ML crash course - Linear regression 이 글을 인용: “In an ML context, linear regression finds the relationship between features and a label.” Self-training (machine learning) “Use the model created in Step 1 to generate predictions (labels) on the unlabeled examples, moving those in whi…” Semi-supervised learning 이 글을 인용: “Self-training is one technique for semi-supervised learning.” Unlabeled example 이 글을 인용: “An example that contains features but no label.” Ground truth (machine learning) 이 글을 인용: “If the label is a matter of human opinion, how can we be sure that each human rater is evaluating events in the…” Labeled example 이 글을 인용: “An example that contains one or more features and a label.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…” 모델 (기계학습) “In supervised machine learning, a model takes an example as input and infers a prediction as output.” Epoch (machine learning) “A full training pass over the entire training set such that each example has been processed once.” Downsampling (machine learning) “For example, in a class-imbalanced dataset, models tend to learn a lot about the majority class and not enough …” Class (machine learning) 이 글을 참조 Generalization (machine learning) “To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data i…” Iteration (machine learning) “A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate.” 기계학습 “2023-12-27 - CI/CD for Machine Learning in 2024: Best Practices to Build, Test, and Deploy” L1 loss 이 글을 인용: “A loss function that calculates the absolute value of the difference between actual label values and the values…” Interpretability (machine learning) “Most linear regression models, for example, are highly interpretable.” Parameter (machine learning) “For example, learning rate is a hyperparameter.”