The values of one row of features and possibly a label .1
Examples in supervised learning fall into two general categories:
A labeled example consists of one or more features and a label. Labeled examples are used during training.
An unlabeled example consists of one or more features but no label. Unlabeled examples are used during inference.
The row of a dataset is typically the raw source for an example. That is, an example typically consists of a subset of the columns in the dataset. Furthermore, the features in an example can also include synthetic features , such as feature crosses.
목차 목차 관련 글 Self-training (machine learning) “The ratio of unlabeled examples to labeled examples in the dataset is high.” ML crash course - Linear regression “In an ML context, linear regression finds the relationship between features and a label.” Epoch (machine learning) 이 글을 인용: “A full training pass over the entire training set such that each example has been processed once.” Semi-supervised learning “Semi-supervised learning can be useful if labels are expensive to obtain but unlabeled examples are plentiful.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] 모델 (기계학습) 이 글을 인용: “In supervised machine learning, a model takes an example as input and infers a prediction as output.” Minority class (machine learning) 이 글을 인용: “A training set with a million examples sounds impressive.” Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…” Clipping (machine learning) “For example, suppose that <0.5% of values for a particular feature fall outside the range 40–60.” Ground truth (machine learning) “Suppose the label is a floating-point value measured by instruments (for example, barometers).” Generalization (machine learning) 이 글을 인용: “You train a model on the examples in the training set.” Downsampling (machine learning) “For example, before training an image recognition model, downsampling high-resolution images to a lower-resolut…” Sparse features “In machine learning, a surprising number of features are sparse features.” 배치 크기 이 글을 인용: “The number of examples in a batch.” Iteration (machine learning) “The batch size determines how many examples the model processes in a single iteration.” 기계학습 “2024-08-24 - What's Really Going On in Machine Learning?” Interpretability (machine learning) “Most linear regression models, for example, are highly interpretable.” Parameter (machine learning) “In contrast, hyperparameters are the values that you (or a hyperparameter tuning service) supply to the model.” Learning rate “For example, a learning rate of 0.3 would adjust weights and biases three times more powerfully than a learning…” Batch (machine learning) 이 글을 인용: “The set of examples used in one training iteration.”