Drew the training data from internal data

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Reference no: EM133482318

Read the following case. To classify prospects as purchasers or non-purchasers, a retail company drew the training data from internal data that include demographic and purchase information and built a model.
- The current model shows a 97% accuracy in the training set and 82% accuracy in the test set.
- The current predictors in the model are customers' demographic variables. To improve the model's performance, analyst A explored additional predictors. "Yeah, it works!" she said excitedly when she found that "refund issued," the new predictor, increased the performance in the test set a lot.
- As the proportion of non-purchasers is much smaller than the purchasers, analyst B decided to try oversampling. What he did was 1) replicated the data points of purchasers until the proportion is similar to the non-purchaser's, and 2) split data into 80% training and 20% test sets randomly. Surprisingly, he found model's performance in the test set was much better and decided to apply his method.
Please address the following questions in individual paragraphs:
- Is the performance of current model good? Please explain why it is good or why it is not good.
- Do you agree with the analyst A? Elaborate why is that.
- Do you agree with the analyst B? Elaborate why is that.

Reference no: EM133482318

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