By Galit Shmueli
Info Mining for company Analytics: thoughts, recommendations, and functions in XLMiner®, 3rd variation offers an utilized method of information mining and predictive analytics with transparent exposition, hands-on routines, and real-life case stories. Readers will paintings with all the ordinary info mining tools utilizing the Microsoft® workplace Excel® add-in XLMiner® to boost predictive types and tips on how to receive company price from sizeable information. that includes up-to-date topical assurance on textual content mining, social community research, collaborative filtering, ensemble equipment, uplift modeling and extra. facts Mining for enterprise Analytics: recommendations, concepts, and purposes in XLMiner®, 3rd variation is a perfect textbook for upper-undergraduate and graduate-level classes in addition to expert courses on information mining, predictive modeling, and massive facts analytics. the recent variation can be a different reference for analysts, researchers, and practitioners operating with predictive analytics within the fields of commercial, finance, advertising, laptop technology, and knowledge expertise.
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Additional resources for Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
6 WHY ARE THERE So MANY DIFFERENT METHODS? As can be seen in this book or any other resource on data mining, there are many different method. for prediction and classification. You might ask younelf why they coexist, and whether some are better than others. The answer is that each method has advantages and disadvantages. 1 � � ROAD MAPS TO THIS BOOK Part III (Chapter 5) discusses perfonnance evaluation. Although it contains only one chapter, we discuss a variety of topics, from predictive performance metrics to misc1assification costs.
To consider why norrnalizing or scaling to [0,1] might be necessary, consider the case of clustering. Clustering typically involves calcularing a distance measure that reflects how far each record is from a cluster center or from other records. With multiple variables, different units will be used: days, dollars, counts, and so on. If the dollars are in the thousands and everything else is in the tens, the dollar variable will come to dominate the distance measure. Moreover, changing units from, say, days to hours or months could alter the outcome completely.
The same training partition is generally used to develop multiple models. Validation Partition The validation partition (sometimes called the test partition) is used to assess the predictive performance of each model so that you can compare models and choose the best one. , PREDICTIVE POWER AND OVERFITTING classification and regression trees, k-nearest-neighbors), the validation partition may be used in an automated fashion to tune and improve the model. Test Partition The test partition (sometimes called the holdout or evaluation partition) is used to assess the performance of the chosen model with new data.