Classification and Data Mining


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The insurance customers already belong to a certain class: they are 'classified' as 'lapsed" or 'has not lapsed'.

The company can use the Classification mining function to create a risk group profile in the form of a data mining model. This profile, or model, contains the common attribute values of the lapsed customers, compared to the other customers.

What is Classification? What is a Classifier?

The insurance company can then apply this profile to new customers as yet 'unclassified' to ascertain if they belong to the risk group. With the Classification algorithms, you can create, validate, or test classification models. For example, you can analyze why a certain classification was made, or you can predict a classification for new data. The task flow looks like this: The insurance company uses an Intelligent Miner classification training run to identify typical combinations of attribute values of each defined customer risk class, and to create a model.

The insurer can use Intelligent Miner to test the accuracy of this model by applying the model to test data with known customer risk classes. A classification model is tested by applying it to test data with known target values and comparing the predicted values with the known values.

The test data must be compatible with the data used to build the model and must be prepared in the same way that the build data was prepared. Typically the build data and test data come from the same historical data set. A percentage of the records is used to build the model; the remaining records are used to test the model. Test metrics are used to assess how accurately the model predicts the known values. If the model performs well and meets the business requirements, it can then be applied to new data to predict the future.

Accuracy refers to the percentage of correct predictions made by the model when compared with the actual classifications in the test data.

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Figure shows the accuracy of a binary classification model in Oracle Data Miner. A confusion matrix displays the number of correct and incorrect predictions made by the model compared with the actual classifications in the test data. The matrix is n -by- n , where n is the number of classes. Figure shows a confusion matrix for a binary classification model. The rows present the number of actual classifications in the test data.

The columns present the number of predicted classifications made by the model. The following can be computed from this confusion matrix:. Lift measures the degree to which the predictions of a classification model are better than randomly-generated predictions. Lift applies to binary classification only, and it requires the designation of a positive class. See "Positive and Negative Classes". If the model itself does not have a binary target, you can compute lift by designating one class as positive and combining all the other classes together as one negative class.

Numerous statistics can be calculated to support the notion of lift. Basically, lift can be understood as a ratio of two percentages: the percentage of correct positive classifications made by the model to the percentage of actual positive classifications in the test data. The resulting lift would be 1. Lift is computed against quantiles that each contain the same number of cases. The data is divided into quantiles after it is scored. It is ranked by probability of the positive class from highest to lowest, so that the highest concentration of positive predictions is in the top quantiles.

A typical number of quantiles is Lift is commonly used to measure the performance of response models in marketing applications. The purpose of a response model is to identify segments of the population with potentially high concentrations of positive responders to a marketing campaign.

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Lift reveals how much of the population must be solicited to obtain the highest percentage of potential responders. Probability threshold for a quantile n is the minimum probability for the positive target to be included in this quantile or any preceding quantiles quantiles n -1, n -2, If a cost matrix is used, a cost threshold is reported instead. The cost threshold is the maximum cost for the positive target to be included in this quantile or any of the preceding quantiles.

See "Costs". Cumulative gain is the ratio of the cumulative number of positive targets to the total number of positive targets. Target density of a quantile is the number of true positive instances in that quantile divided by the total number of instances in the quantile.

Cumulative target density for quantile n is the target density computed over the first n quantiles. Quantile lift is the ratio of target density for the quantile to the target density over all the test data. Cumulative percentage of records for a quantile is the percentage of all cases represented by the first n quantiles, starting at the end that is most confidently positive, up to and including the given quantile.

C umulative number of targets for quantile n is the number of true positive instances in the first n quantiles. Cumulative number of nontargets is the number of actually negative instances in the first n quantiles. Cumulative lift for a quantile is the ratio of the cumulative target density to the target density over all the test data.

ROC is another metric for comparing predicted and actual target values in a classification model. ROC, like lift, applies to binary classification and requires the designation of a positive class. You can use ROC to gain insight into the decision-making ability of the model.


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How likely is the model to accurately predict the negative or the positive class? ROC measures the impact of changes in the probability threshold. The probability threshold is the decision point used by the model for classification. The default probability threshold for binary classification is. In multiclass classification, the predicted class is the one predicted with the highest probability. ROC can be plotted as a curve on an X-Y axis. The false positive rate is placed on the X axis.

Basic Concept of Classification (Data Mining)

The true positive rate is placed on the Y axis. The top left corner is the optimal location on an ROC graph, indicating a high true positive rate and a low false positive rate. The larger the AUC, the higher the likelihood that an actual positive case will be assigned a higher probability of being positive than an actual negative case.

The AUC measure is especially useful for data sets with unbalanced target distribution one target class dominates the other. Changes in the probability threshold affect the predictions made by the model. For instance, if the threshold for predicting the positive class is changed from. This will affect the distribution of values in the confusion matrix: the number of true and false positives and true and false negatives will all be different. The ROC curve for a model represents all the possible combinations of values in its confusion matrix. You can use ROC to find the probability thresholds that yield the highest overall accuracy or the highest per-class accuracy.

For example, if it is important to you to accurately predict the positive class, but you don't care about prediction errors for the negative class, you could lower the threshold for the positive class. However, the U. Safe Harbor Principles currently effectively expose European users to privacy exploitation by U. As a consequence of Edward Snowden 's global surveillance disclosure , there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency , and attempts to reach an agreement have failed.

The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals.

Use of data mining by the majority of businesses in the U. Due to a lack of flexibilities in European copyright and database law , the mining of in-copyright works such as web mining without the permission of the copyright owner is not legal. Where a database is pure data in Europe there is likely to be no copyright, but database rights may exist so data mining becomes subject to regulations by the Database Directive. On the recommendation of the Hargreaves review this led to the UK government to amend its copyright law in [37] to allow content mining as a limitation and exception.

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