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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 [...]

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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 [...]

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 [...]

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 [...]

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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 [...]

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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: