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source/classification2.Rmd
@@ -1234,7 +1234,7 @@ each estimate having a standard error around `r round(mean(100*accuracies$std_er
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Since the cross-validation accuracy estimates the test set accuracy,
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the fact that the test set accuracy also doesn't change much is expected.
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Also note that the $K =$ 3 model had
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-a precision of `r round(100*cancer_prec_1$.estimate, 0)`% and
+a precision of `r round(100*cancer_prec_1$.estimate, 0)`% and
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recall of `r round(100*cancer_rec_1$.estimate, 0)`%,
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while the tuned model had
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a precision of `r round(100*cancer_prec_tuned, 0)`% and recall of `r round(100*cancer_rec_tuned, 0)`%.
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