Machine learning, computational learning theory, and similar terms are often used in the context of Data Mining to denote the application of generic model-fitting or classification algorithms for predictive data mining. Unlike traditional statistical data analysis, which is usually concerned with the estimation of population parameters by statistical inference, the emphasis in data mining (and machine learning) is usually on the accuracy of prediction (predicted classification) regardless of whether the "models" or techniques that are used to generate the prediction are interpretable or open to simple explanation. Good examples of this type of technique often applied to predictive data mining are neural networks or meta-learning techniques such as boosting, etc. These methods usually involve the fitting of very complex "generic" models that are not related to any reasoning or theoretical understanding of underlying causal processes; instead, these techniques can be shown to generate accurate predictions or classification in crossvalidation samples.