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lizarova777
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Dec 5, 2019
| mean(M2$certified!=M2$predict2) | ||
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| ### According to the table, the pruned tree does a better job in making predictions about the the students in the second data set than the original tree. The pruned tree has a lower error rate (0.4637). However, both of the trees have high error rates thus the model is not ideal for our data. Thus we might need to modify the model by including other variables. |
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Good job! What is the error rate of the original tree?
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| ### The first model uses raw variables (SAT total score and high school GPA) to predict the outcome variable (gender), while the second model features an extract variable from the data (ACT total score) instead of SAT. | ||
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| ### Model 1 has a slightly lower error rate comparing to Model 2, which indicates that SAT total score has higher accuracy of predicting the student's gender than ACT total score. However, both of the trees have high error rates thus the model is not ideal for our data. Thus we might need to modify the model by taking other variables into consideration. |
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What are the error rates of the models?
| ### The first model uses raw variables (SAT total score and high school GPA) to predict the outcome variable (gender), while the second model features an extract variable from the data (ACT total score) instead of SAT. | ||
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| ### Model 1 has a slightly lower error rate comparing to Model 2, which indicates that SAT total score has higher accuracy of predicting the student's gender than ACT total score. However, both of the trees have high error rates thus the model is not ideal for our data. Thus we might need to modify the model by taking other variables into consideration. | ||
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Yi Yang