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A goal of machine learning or deep learning can be to generalize from the experience. Generalization can be the ability of a learning machine or system to perform accurately on new, unseen inputs after having been trained on a training data set. Training examples can come from an unknown probability distribution and the learning machine or system can be tasked to build an input and output model that enables it to produce sufficiently accurate predictions with new inputs. Bounds or limits, e.g. probabilitistic bounds, on the performance, accuracy and reproducibility of machine learning can be determined. When machine learning is used for solving clinical problems, e.g. outcome prediction or treatment planning or modification, the bounds or limits of performance, accuracy and/or reproducibility of the machine learning system or learning machine can influence and/or determine the performance, accuracy, and/or reproducibility of the clinical application, e.g. outcome prediction or treatment planning or modification. Accuracy can include the assessment of true positives, true negatives, false positives and false negatives. Reproducibility can be precision. Performance of the machine learning system and/or learning machine can include other statistical measures known in the art for assessing the performance of a clinical system.

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