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The predicted value of an SVR prediction may have a maximum limit due to the settings of the hyperparameters of the SVR model and the nature of the data used. The maximum limit may be caused by the choice of the kernel function, C parameter, or epsilon parameter, which can constrain the predictions to a specific range. For example, an SVR model with a linear kernel function will have a maximum limit on its predictions, while a polynomial or radial basis function kernel could allow for more flexibility in prediction values. Additionally, the nature of the data used can also affect the maximum limit of the predicted values. For instance, if the feature space of the data is limited or has a known upper bound, it may constrain the range of values that the SVR prediction can produce.