During training or testing, a mathematical function that calculates the loss on a batch of examples . A loss function returns a lower loss for models that makes good predictions than for models that make bad predictions.1
The goal of training is typically to minimize the loss that a loss function returns.1
Many different kinds of loss functions exist. Pick the appropriate loss function for the kind of model you are building. For example:1
목차 목차 관련 글 ML crash course - Logistic regression 이 글을 인용: “Logistic regression models use log loss as the loss function instead of squared loss.” ML crash course - Linear regression 이 글을 인용: “The loss functions for linear models always produce a convex surface.” Convex function 이 글을 인용: “A lot of the common loss functions, including the following, are convex functions:” Training loss 이 글을 인용: “For example, suppose the loss function is MSE.” L1 loss 이 글을 인용: “A loss function that calculates the absolute value of the difference between actual label values and the values…” 긴 함수 관련 절: 함수를 작게 나누면 생기는 문제들에 대해 mini-batch “For example, suppose the entire training set (the full batch) consists of 1,000 examples.” Sigmoid function “The sigmoid function has several uses in machine learning, including:” 회귀 모형 관련 절: Types of regression models Test loss “When building a model, you typically try to minimize test loss.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] 함수 (수학) “A function from a set to a set assigns to each element of exactly one element of .” 선형 모델 “Linear regression and logistic regression are two types of linear models.” Test set “Because the test set is only indirectly associated with training, test loss is a less biased, higher quality me…” Loss curve “A plot of loss as a function of the number of training iterations.” 모델 (기계학습) “Unsupervised machine learning also generates models, typically a function that can map an input example to the …” Iteration (machine learning) “A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate.” 배치 크기 “Full batch, in which the batch size is the number of examples in the entire training set.” Regularization (machine learning) “The goal isn't to minimize training loss.” Loss (machine learning) 이 글을 인용: “During the training of a supervised learning, a measure of how far a model's prediction is from its label.”