Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.The following code sets up the environment for these examples by loading the required packages and the dataset, as well as ... with the opel and saab classes aggregated to a new car class, and the bus and van classes aggregated to a new other class. ... 7.2.3, and 7.2.4 in Chapter 7, which calculate the misclassification error, mean misclassification cost, and confusion matrix, dmr.claseval respectively.
|Title||:||Data Mining Algorithms|
|Publisher||:||John Wiley & Sons - 2014-11-17|