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In Support Vector Machines classifications, weights are a biasing mechanism for specifying the relative importance of target values (classes). SVM models are automatically initialized to achieve the best average prediction across all classes. However, if the training data does not represent a realistic distribution, then you can bias the model to compensate for class values that are underrepresented. If you increase the weight for a class, then the percentage of correct predictions for that class should increase.