The subset of the dataset that performs initial evaluation against a trained model . Typically, you evaluate the trained model against the validation set several times before evaluating the model against the test set .
Traditionally, you divide the examples in the dataset into the following three distinct subsets:
Ideally, each example in the dataset should belong to only one of the preceding subsets. For example, a single example shouldn’t belong to both the training set and the validation set.1
목차 목차 관련 글 Early stopping 이 글을 인용: “In early stopping, you intentionally stop training the model when the loss on a validation dataset starts to in…” Test loss “That's because a low test loss is a stronger quality signal than a low training loss or low validation loss.” Set-based development “In Set-based development, communication is about constraints, not choices.” Self-training (machine learning) “The ratio of unlabeled examples to labeled examples in the dataset is high.” Validation loss 이 글을 인용: “A metric representing a model's loss on the validation set during a particular iteration of training.” Epoch (machine learning) “A full training pass over the entire training set such that each example has been processed once.” Generalization (machine learning) “To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data i…” 지도 학습 “When we evaluate a model, we use a labeled dataset, but we only give the model the dataset's features.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] mini-batch “For example, suppose the entire training set (the full batch) consists of 1,000 examples.” Minority class (machine learning) “A training set with a million examples sounds impressive.” Example (machine learning) “That is, an example typically consists of a subset of the columns in the dataset.” Downsampling (machine learning) “Training on a disproportionately low percentage of over-represented class examples in order to improve model tr…”