By Lior Rokach
Determination bushes became probably the most strong and well known ways in wisdom discovery and information mining; it's the technology of exploring huge and complicated our bodies of knowledge in an effort to realize important styles. choice tree studying keeps to conform through the years. current tools are always being better and new tools introduced.
This 2d version is devoted fullyyt to the sphere of determination timber in facts mining; to hide all facets of this crucial strategy, in addition to greater or new tools and strategies constructed after the book of our first version. during this re-creation, all chapters were revised and new subject matters introduced in. New issues contain Cost-Sensitive energetic studying, studying with doubtful and Imbalanced facts, utilizing selection bushes past type initiatives, privateness protecting selection Tree studying, classes discovered from Comparative stories, and studying choice bushes for giant facts. A walk-through advisor to present open-source info mining software program can be integrated during this edition.
This ebook invitations readers to discover the numerous merits in information mining that call timber offer:
- Self-explanatory and straightforward to stick with while compacted
- Able to address a number of enter info: nominal, numeric and textual
- Scales good to important data
- Able to technique datasets that could have error or lacking values
- High predictive functionality for a comparatively small computational effort
- Available in lots of open resource info mining applications over numerous platforms
- Useful for numerous initiatives, reminiscent of category, regression, clustering and have selection
Readership: Researchers, graduate and undergraduate scholars in details platforms, engineering, desktop technology, facts and administration.
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Extra info for Data Mining With Decision Trees: Theory and Applications (2nd Edition)
1 illustrates a typical precision-recall graph. This twodimensional graph is closely related to the well-known receiver operating characteristics (ROC) graphs in which the true positive rate (recall) is plotted on the Y -axis and the false positive rate is plotted on the X-axis [Ferri et al. (2002)]. However, unlike the precision-recall graph, the ROC diagram is always convex. Given a probabilistic classiﬁer, this trade-oﬀ graph may be obtained by setting diﬀerent threshold values. 5. 5, the trade-oﬀ graph can be obtained.
These techniques include the most common methods of traditional statistics, like the goodness-of-ﬁt test, the t-test of means and analysis of variance. These methods are not as much related to data mining as are their discovery-oriented counterparts because most data mining problems are concerned with selecting a hypothesis (out of a set of hypotheses) rather than testing a known one. While one of the main objectives of data mining is model identiﬁcation, statistical methods usually focus on model estimation [Elder and Pregibon (1996)].
4 A typical ROC curve. 2 19:12 Data Mining with Decision Trees (2nd Edition) - 9in x 6in b1856-ch04 Data Mining with Decision Trees Hit-Rate Curve The hit-rate curve presents the hit ratio as a function of the quota size. Hitrate is calculated by counting the actual positive labeled instances inside a determined quota [An and Wang (2001)]. 10) where t[k] represents the truly expected outcome of the instance located in the k’th position when the instances are sorted according to their conditional probability for “positive” by descending order.