Zhongyuan Zhang Assignment6#143
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| #accuracy rate | ||
| mean(M2$certified==M2$predict1)#21.9% | ||
| mean(M2$certified==M2$predict2)#53.6% |
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What does this imply about the pruned tree?
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| a mean difference of 0.216 in GPAO from model2's prediction | ||
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| Therefore, model2 did a better job in prediction |
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What does this suggest about the second model's generalizability? Great job overall! Keep up the good work.
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| df_num<-data.frame(scale(df_num)) | ||
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| pca <- prcomp(df_num, scale = TRUE) | ||
| #plot scree plot to decide how many pcs that could be used in the model | ||
| plot(pca,type="lines") | ||
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| ``` | ||
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| ```{R} | ||
| # according to the result, i decided to keep PC1,PC2 | ||
| pca1<-data.frame(pca[["x"]]) | ||
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| dff<-cbind(df,pca1) | ||
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| ### To Submit Your Assignment | ||
| model2<-rpart(GPAO~PC1+PC2,dff, method = "anova") |
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Thank you for your work!!
Best!