![]() positive, negative, negative, positive, positive, positive, negative These are called the ground-truth labels of the sample. Here is an example of the labels for seven samples used to train the model. if it is about classifying student test scores).Īssume there is a binary classification problem with the classes positive and negative. ![]() if the problem is about cancer classification), or success or failure (e.g. More specifically, the two class labels might be something like malignant or benign (e.g. ![]() Generally these two classes are assigned labels like 1 and 0, or positive and negative. ![]() In binary classification each input sample is assigned to one of two classes. Run on gradient Confusion Matrix for Binary Classification
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