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5. The further you go into the future, the less you can rely on the forecasts. However, this also depends on the algorithm you use for forecasting. The ARIMA algorithm is much better than ART for long-term forecasting.

Figure 2-8

can order the products that are commonly in a basket close together to help your customers remember to buy all, rather than just one, of them in a single purchase.

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Boolean. We can assign to this variable any function that matches that signature, and indeed in the very next line we do just that when we assign testFunc to func, using what looks like a standard variable assignment. We can now call testFunc() by way of our variable reference to it, which the code does twice just to prove it works. Passing functions to other functions works along similar lines (see listing 3.14). The receiving function uses a function signature for its parameters, just like the variable in listing 3.13.

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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.

1. In the Properties window, choose Anchor and click the drop-down box. The Anchor property visual interface appears. 2. Click the bars indicating the edges you want to anchor. When finished, click out side of the Anchor property interface.

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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A lift chart is the most popular way to show the performance of predictive models. It is very intuitive and easy to understand. Figure 9-12 shows a lift chart for the predictive models you created in Lesson 1 for the value 1 (buyers) of the predicted variable (Bike Buyer), without the filtered Decision Trees model, which was removed to get a less cluttered picture.

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