The x-axis represents the distance from the boundary of any single instance, and the y-axis represents the loss size, or penalty, that the function will incur depending on its distance. • "er" expectile regression loss. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss. Hinge loss 的叫法来源于其损失函数的图形，为一个折线，通用的函数表达式为： method a character string specifying the loss function to use, valid options are: • "hhsvm" Huberized squared hinge loss, • "sqsvm" Squared hinge loss, • "logit" logistic loss, • "ls" least square loss. Theorem 2. loss {‘hinge’, ‘squared_hinge’}, default=’squared_hinge’ Specifies the loss function. Here is a really good visualisation of what it looks like. Square loss is more commonly used in regression, but it can be utilized for classification by re-writing as a function . Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions.. A loss function, in the context of Machine Learning and Deep Learning, allows us to quantify how “good” or “bad” a given classification function (also called a “scoring function”) is at correctly classifying data points in our dataset. The square loss function is both convex and smooth and matches the 0–1 when and when . LinearSVC is actually minimizing squared hinge loss, instead of just hinge loss, furthermore, it penalizes size of the bias (which is not SVM), for more details refer to other question: Under what parameters are SVC and LinearSVC in scikit-learn equivalent? ‘hinge’ is the standard SVM loss (used e.g. 指数损失（Exponential Loss） ：主要用于Adaboost 集成学习算法中； 5. It is purely problem specific. There are several different common loss functions to choose from: the cross-entropy loss, the mean-squared error, the huber loss, and the hinge loss – just to name a few.” Some Thoughts About The Design Of Loss Functions (Paper) – “The choice and design of loss functions is discussed. Understanding Ranking Loss, Contrastive Loss, Margin Loss, Triplet Loss, Hinge Loss and all those confusing names. #FOR COMPILING model.compile(loss='squared_hinge', optimizer='sgd') # optimizer can be substituted for another one #FOR EVALUATING keras.losses.squared_hinge(y_true, y_pred) Hinge Loss. The hinge loss is a loss function used for training classifiers, most notably the SVM. After the success of my post Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names, and after checking that Triplet Loss outperforms Cross-Entropy Loss in my main research … Square Loss. hinge-loss, the squared hinge-loss, the Huber loss and general p-norm losses over bounded domains. dual bool, default=True So which one to use? 其他损失（如0-1损失，绝对值损失） 2.1 Hinge loss. Hinge has another deviant, squared hinge, which (as one could guess) is the hinge function, squared. 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