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As you have learned up to now in this chapter, to find the best solution for your company, you need to try to resolve a business problem by using a number of data mining models, using different algorithms and different algorithm parameters. You should deploy into production the model that performs best. In this lesson, you will learn how to evaluate the performance of the different models. Generally, you can measure a model s accuracy, reliability, and usefulness. Accuracy is probably the most used measure, determining how well an outcome from a model correlates with real data. Reliability assesses how well a data mining model performs on different datasets in other words, how robust a model is. You can trust a model if it generates similar predictions or finds similar patterns regardless of the test data that is supplied. If a model creates good predictions for one subset of data but worse ones for other subsets, you cannot trust the model for general predictions. Usefulness measures how helpful the information gathered with data mining is. Usefulness is typically measured through the perception of business users, using questionnaires and similar means.



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<form method="POST" action="getCustomer.aspx"> Enter Customer ID: <input type="text" name="Id"> <input type="submit" value="Get Customer"> </form>





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This example form prompts the user for a customer ID, displays a text box that collects the desired customer ID, and also displays a Submit button that initiates the sending of data to the web server. The method attribute of the form tag indicates the HTTP command (POST) to use when sending the request to the server. The action attribute is the relative URL of the page to which the request will be sent. There are two HTTP commands that can be used to submit the form data to the web server: GET and POST. When the GET command is used, the form data is appended to the URL as part of the query string. The query string is a collection of key value pairs, separated by an ampersand (&) character. The start of the query string is indicated by a question mark ( ). The following provides an example.

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Testing the accuracy of predictive models is simple. The model that predicts most accurately has performed the best. So how can you check the quality of the predictions Remember that when you prepare the data, you split it into training sets and test sets. You train the model using the training set, and then you use the model to make predictions on the test set. Because you already know the value of the target variable in the test set, you can measure how accurate the predictions are. Because this measurement of predictive models is a recurring task, standard methods of showing the quality of predictions have evolved. These standard methods include lift charts, profit charts, and classification matrices. In SSAS 2008, you can test the reliability of predictive models using cross validation. With cross validation, you partition your training dataset into many smaller sections. SSAS then creates multiple models on the cross sections using one section at a time as test data and other sections as training data, trains the models, and creates many different accuracy measures across partitions. If the measures across different partitions differ widely, the model is not robust on different training and test set combinations.

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