fundamentals of machine learning (continuedprediction error or loss value-- measure of the distance between your model' prediction and the target classes-- set of possible labels to choose from in classification problem for examplewhen classifying cat and dog picturesdogand catare the two classes label-- specific instance of class annotation in classification problem for instanceif picture # is annotated as containing the class dog,then dogis label of picture # ground-truth or annotations--all targets for datasettypically collected by humans binary classification-- classification task where each input sample should be categorized into two exclusive categories multiclass classification-- classification task where each input sample should be categorized into more than two categoriesfor instanceclassifying handwritten digits multilabel classification-- classification task where each input sample can be assigned multiple labels for instancea given image may contain both cat and dog and should be annotated both with the catlabel and the doglabel the number of labels per image is usually variable scalar regression-- task where the target is continuous scalar value predicting house prices is good examplethe different target prices form continuous space vector regression-- task where the target is set of continuous valuesfor examplea continuous vector if you're doing regression against multiple values (such as the coordinates of bounding box in an image)then you're doing vector regression mini-batch or batch-- small set of samples (typically between and that are processed simultaneously by the model the number of samples is often power of to facilitate memory allocation on gpu when traininga mini-batch is used to compute single gradient-descent update applied to the weights of the model licensed to