Merge branch 'confusion-precision-recall' of github.com:UBC-DSCI/introduction-to-datascience into confusion-precision-recall

Author: trevorcampbell

source/classification2.Rmd

@@ -160,7 +160,12 @@ classifier can make, corresponding to the four entries in the confusion matrix:
 - **True Negative:** A benign observation that was classified as benign (bottom right in Table \@ref(tab:confusion-matrix)).
 - **False Negative:** A malignant observation that was classified as benign (bottom left in Table \@ref(tab:confusion-matrix)).
 
-And then rather than just looking at accuracy, we can also evaluate the **precision** and **recall** of the classifier.
+A perfect classifier would have zero False Negatives and False Positives.
+However,
+in practice there will always be some error in the model,
+so it is important to think about what type of errors we think are more harmful,
+which we discuss in detail in section \@ref(critically-analyze-performance)
+Two commonly used metrics that capture part of what the confusion matrix is telling us are the **precision** and **recall** of the classifier which are often reported together with accuracy.
 *Precision* quantifies how many of the positive predictions the classifier made were actually positive. Intuitively,
 we would like a classifier to have a *high* precision: for a classifier with high precision, if the 
 classifier reports that a new observation is positive, we can trust that that