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Lets return to the adult data set for an example of how to calculate the J -measure. We applied Clementines GRI algorithm to the categorical variables in the data set, again specifying minimum support of 10% and minimum confidence of 75%, and setting the rule table maximum size to 30. The results are shown in Figure 10.3. Lets find the J -measure for the sixth association rule in Figure 10.3: If Sex = Female and Marital Status = Never married, then Work Class = Private, with confidence of 76.3% and support of 11.1% (not 14.6%). We need the following statistics: (more…)

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EPILOGUE
Weve Only Just Begun An Invitation to Data Mining Methods and Models
I hope you have enjoyed Discovering Knowledge in Data: An Introduction to Data Mining, and that our experience together has whetted your appetite for learning more about this unique and powerful field of study. In fact, it is true that we have only just begun our exploration of data mining. More volumes in thisWiley Interscience data mining series await your examination. (more…)

In Olympic figure skating, the best-performing skater is not selected by a single judge alone. Instead, a suite of several judges is called upon to select the best skater from among all the candidate skaters. Similarly in model selection, whenever possible, the analyst should not depend solely on a single data mining method. Instead, he or she should seek a confluence of results from a suite of different data mining models. (more…)

In Chapter 1 the graphic representing the CRISPDMstandard process for data mining contained a feedback loop between the model building and evaluation phases. In Chapter 5 (Figure 5.1) we presented a methodology for supervised modeling. Where do the methods for model evaluation from Chapter 11 fit into these processes? (more…)

Lift charts are often presented in their cumulative form, where they are denoted as cumulative lift charts, or gains charts. The gains chart associated with the lift chart in Figure 11.5 is presented in Figure 11.6. The diagonal on the gains chart is analogous to the horizontal axis at lift = 1 on the lift chart. Analysts would like to see gains charts where the upper curve rises steeply as one moves from left to right and then gradually flattens out. In other words, one prefers a deeper bowl to a shallower bowl. How do you read a gains chart? Suppose that we canvassed the top 20% of our contact list (percentile = 20). By doing so, we could expect to reach about 62% of the total number of high-income persons on the list. Would doubling our effort also double our results? No. Canvassing the top 40% on the list would enable us to reach
approximately 85% of the high-income persons on the list. Past this point, the law of diminishing returns is strongly in effect.

Lift charts and gains charts can also be used to compare model performance. Figure 11.7 shows the combined lift chart for models 1 and 2. The figure shows that when it comes to model selection, a particular model may not be uniformly preferable. For example, up to about the 6th percentile, there appears to be no apparent difference in model lift. Then, up to approximately the 17th percentile, model 2 is preferable,
provided slightly higher lift. Thereafter, model 1 is preferable.

Hence, if the goal were to canvass up to the top 17% or so of the people on the contact list with high incomes, model 2 would probably be selected. However, if the goal were to extend the reach of the marketing initiative to 20% or more of the likely contacts with high income, model 1 would probably be selected. This question of multiple models and model choice is an important one, which we spend much time discussing in Reference 1.

Taken From : DISCOVERING KNOWLEDGE IN DATA An Introduction to Data Mining

Lift is a function of sample size, which is why we had to specify that the lift of 3.17 for model 1 was measured at n = 4824 records. When calculating lift, the software will first sort the records by the probability of being classified positive. The lift is then calculated for every sample size from n = 1 to n = the size of the data set. On the other hand, if the project required 60% of all records, the lift would
have faA chart is then produced which graphs lift against the percentile of the data set. (more…)

Lift charts and gains charts are graphical evaluative methods for assessing and comparing the usefulness of classification models. Lift is a concept, originally from the marketing field, which seeks to compare the response rates with and without using the classification model.We shall explore these concepts by continuing our examination of the C5.0 models for classifying income. (more…)