A subset of the dataset reserved for testing a trained model . Traditionally, you divide examples in the dataset into the following three distinct subsets:1
Each example in a dataset should belong to only one of the preceding subsets. For instance, a single example shouldn’t belong to both the training set and the test set.1
The training set and validation set are both closely tied to training a model. Because the test set is only indirectly associated with training, test loss is a less biased, higher quality metric than training loss or validation loss .1
목차 목차 관련 글 ML crash course - Linear regression “Mean squared error: The average of L2 losses across a set of examples.” Early stopping “In early stopping, you intentionally stop training the model when the loss on a validation dataset starts to in…” Loss curve “A plot of loss as a function of the number of training iterations.” Self-training (machine learning) “The ratio of unlabeled examples to labeled examples in the dataset is high.” mini-batch “For example, suppose the entire training set (the full batch) consists of 1,000 examples.” Log loss “is the predicted value (somewhere between 0 and 1, exclusive), given the set of features in .” 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…” 손실 함수 “The goal of training is typically to minimize the loss that a loss function returns.” Regularization rate “The following simplified loss equation shows lambda's influence:” Iteration (machine learning) “A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate.” Generalization curve “A plot of both training loss and validation loss as a function of the number of iterations.” Downsampling (machine learning) “Downsampling helps balance the amount of training on the majority and minority classes.” Minority class (machine learning) “A training set with a million examples sounds impressive.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] L1 loss “L1 loss is less sensitive to outliers than L2 loss.” Loss (machine learning) “During the training of a supervised learning, a measure of how far a model's prediction is from its label.” Example (machine learning) “Labeled examples are used during training.” 지도 학습 “Training a model from features and their corresponding labels.” 배치 크기 “For instance, if the training set contains a million examples, then the batch size would be a million examples.”