Download MSDS 420 Module 8 Discussion

MSDS 420 Module 8 Discussion
When assessing the effectiveness of the information retrieval (IR) system, precision gives the fraction of
the returned results that are relevant to the information needed. Recall gives the fraction of the
relevant documents in the collection that were returned by the system. To illustrate how these two
statistics play a complementary role in effectivity measures, we can start by looking at the confusion
matrix shown in Table 1 (Ting, 2011).
Actual Class
Positive
Negative
Assigned Class
Positive
True Positive (TP)
False Positive (FP)
Negative
False Negative (FN)
True Negative (TN)
Table 1: Confusion Matrix
From this confusion matrix, we can mathematically define Precision (positive predicted value) and Recall
as follows:
Precision = True Positives/Total number of actual positives = TP/(TP + FN)
Recall = True Positives/Total number of positives predicted = TP/(TP + FP)
Effectively, these two measures tell us how valid the results are and how complete the results are. One
figure that helped me visualize these metrics is shown in Figure 1.
MSDS 420 Module 8 Discussion
Figure 1: Precision and Recall Image created by Walber. https://commons.wikimedia.org/wiki/File:Precisionrecall.svg
Instead of two measures, they are often combined to provide a single measure of retrieval performance
called the F-measure:
F-measure = 2 * Recall * Precision/(Recall + Precision)
The inverted index at the end of a textbook is like the inverted index of an IR system. The purpose of
inverted index is to optimize a query’s speed for full-text searches. Textbook indexes are basically
printed inverted indexes that required a tremendous amount of effort to produce.
References
MSDS 420 Module 8 Discussion
Ting K.M. (2011) Precision and Recall. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning.
Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_652