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Thus, the 20,162 classifications (predictions) of income ?50,000 are said to be negatives, and the 4824 classifications of income>50,000 are said to be positives. The 2317 negative classifications that were made in error are said to be false negatives. That is, a false negative represents a record that is classified as negative but is actually positive. Of the 4824 positive classifications, 1157 actually had low incomes, so that there are 1157 false positives. A false positive represents a record that is classified as positive but is actually negative. (more…)

Every people have different opinion about their favorite car but there are certain brands that become preference. One of favorite brand of all is Mercedes Benz. Somebody will proud if has this car. Mercedes is well known with good performance support with luxury appearance. It can never been denied. It has everything both performance and appearance. It always creates new innovation in every series. It provides ultimate features which make it luxury car. People will never stop to watch every time this car is passing on the street. (more…)

Recall from Chapter 6 that we applied a C5.0 model for classifying whether a persons income was low (?50,000) or high (>50,000), based on a set of predictor variables which included capital gain, capital loss, marital status, and so on. Let us evaluate the performance of that decision tree classification model, using the notions of error rate, false positives, and false negatives. (more…)

Perhaps the most widespread usage of supervised data mining involves the classification task. Recall that in classification, there is a target categorical variable. The data mining model examines a large set of records, each record containing information on the target variable as well as a set of input or predictor variables. The analyst would like to be able to generate a classification for the target variable for new records, or persons, (more…)

For estimation and prediction models, which employ supervised methods, we are provided with both the estimated (or predicted) value y of the numeric target variable and the actual value y. Therefore, a natural measure to assess model adequacy is to examine the estimation error, or residual, |y ? y|. Since the average residual is always equal to zero, we cannot use it for model evaluation; some other measure is
needed. (more…)

You know that BusinessFunders.Com offers you with business loan. They offer you around $ 5,000 up to $ 1,000,000 to support your business. They know that financial support is the one problem for the businessman but this is the starting point to develop their business. (more…)

In Chapter 3 we learned how to apply exploratory data analysis (EDA) to learn about the salient characteristics of a data set. EDA represents a popular and powerful technique for applying the descriptive task of data mining. On the other hand, because descriptive techniques make no classifications, predictions, or estimates, an objective method for evaluating the efficacy of these techniques can be elusive. The watchword is common sense. Remember that data mining models should be as transparent as (more…)

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Wedding is like the ultimate party of all. Every people want their wedding ceremony and party as beautiful as they ever imagine. They want to share the moment of happiness with all the guests. They want the moment to be remembered as long as they live. It makes sense if they want everything to be perfect. Wedding party needs lots of preparation and it needs lots of time too. It will be eased if there is wedding organizer which helps to prepare everything but you can not leave everything on wedding organizer hand. This is your party and it will be personalized if there is a little bit taste of yours and your couple. It will be personalized with different favor. The guests will always remember the favor as a piece of your moment. (more…)

As you may recall from Chapter 1, the CRISP cross-industry standard process for data mining consists of six phases, to be applied in an iterative cycle: (more…)