the different

1. Lisa Jung
Developer Advocate @Elastic
Beginner’s Crash Course to Elastic Stack Series
Part 1.2: Understanding the Relevance of your search using
Elasticsearch and Kibana

2. Beginner’s crash course to the Elastic Stack Series
● Part 1.1: Intro to Elasticsearch and Kibana
○ use case of Elasticsearch and Kibana
○ the basic architecture of Elasticsearch
○ perform CRUD(Create, Read, Update, and Delete) operations with
Elasticsearch and Kibana

3. Missed the first workshop? No worries!
● Part 1.1: Intro to Elasticsearch and Kibana
○ Repo: https://ela.st/workshop-1-repo

4. The Elastic Stack
Reliably and securely take data from
any source, in any format, then search,
analyze, and visualize it in real time.

5. Elasticsearch
Store | Search | Analyze

7. Elastic is a search company.
We focus on value to users by producing fast results
that operate at scale and are relevant. This is our
DNA. We believe search is an experience. It is what
defines us, and makes us unique.
Scale,
Relevance

8. Relevance

9. How do we measure relevance?
● Precision
● Recall

10. Store | Search | Analyze
I store data as
documents!
Documents with similar traits
are grouped into an index!
Index
Document Document Document
Document Document
Document

11. When search query is sent, Elasticsearch retrieves relevant
documents and presents the documents as search results.
Index
Document Document Document
Document Document
Document
Search Results

12. These two diagrams depict the same thing!
Index
Index
Document Document Document
Document Document
Document

13. Index
True positives are relevant documents that are
returned to the user.
T
T
T
T
T True positives

14. Index
False positives are irrelevant documents that are
returned to the user.
T
T
T
T
T True positives
F
F False positives

15. Index
True negatives are irrelevant documents that are
not returned to the user.
T
T
T
T
T
T
T
F
F
True negatives
T
T
T
T

16. Index
False negatives are relevant documents that were
not returned to the user.
T
T
T
T
T
T
T
F
F
True negatives
False negatives
T
T
T
T

17. What is precision?
Precision =
True positives
True positives + False positives
What portion of the retrieved data is actually relevant to
the search query?

18. What is recall?
Recall =
True positives
True positives + False negatives
What portion of relevant data is being returned as search
results?
T
T
T

19. Precision and Recall are inversely related
Precision
I want all the
retrieved results to
be a perfect match to
the query, even if it
means returning less
documents.
I want to retrieve
more results even
if documents may
not be a perfect
match to the
query.
Precision Recall
Recall
Precision Recall

20. Precision and recall determine which documents are
included in the search results.
Precision and recall do not determine which of the returned
documents are more relevant compared to the other!

21. Ranking refers to ordering of the results (from most relevant
results at the top, to least relevant at the bottom).
Most Relevant
…
…
…
Less Relevant
…
…
…
Least Relevant
How to form good habits
(Highest Score)
(Lowest Score)

22. What is score?
● The score is a value that represents how relevant a document is to that
specific query
● A score is computed for each document that is a hit

23. What is score?
● Term Frequency(TF)
● Inverse Document Frequency(IDF)

24. What is score?
How to form good habits } Search Query
Hits
Most Relevant
…
…
…
Less Relevant
…
…
…
Least Relevant
(Highest Score)
(Lowest Score)

25. Term Frequency(TF) determines how many times each search
term appears in a document.
How to form good habits
TF= 4
TF= 1
If search terms are found in
high frequency in a
document, the document is
considered more relevant to
the search query.
Search Terms
} Search Query

26. What is Inverse Document Frequency(IDF)?
How to form good habits
How to form a meetup group
Hits
Good chicken recipe
How to form a band
Good times rolling
Good habits 101
Good habits are easy to master!
We may contain some
of the search terms
but we have nothing
to do with forming
good habits!
IDF diminishes the weight of
terms that occur very
frequently in the document
set and increases the weight
of terms that occur rarely!

27. Fine tuning precision or recall using
Elasticsearch and Kibana

28. Click on the link to the workshop repo.
https://ela.st/workshop-2-repo

29. Scroll down to the Resources section & open Free
Elastic Cloud Trial link in a new tab.

30. Scroll down to the Resources section & open Free
Elastic Cloud Trial link in a new tab.

31. Go to the email account you signed up with and
verify your email.

32. Open email from Elastic. Click on verify and accept
button.

33. Enter your password.

34. Click on start your free trial.

35. Select the Elastic Stack.

36. Configure your settings.

37. Name your deployment then create deployment.
Beginner’s Crash Course

38. Save your deployment credentials.

39. Open Kibana.
Beginner’s Crash Course
Beginner’s Crash Course

40. Click on Explore on my own option.

41. Click on Upload a file option

42. Download and Unzip News Category Dataset from Kaggle

43. Drag and drop a file you want to upload.

44. Kibana will give you an analysis of the first 1000 lines of
your data and give you a summary of your dataset.

45. Field section displays fields identified, high level statistics,
and top occuring values

46. Click on import button

47. Name your index and click on import.

48. Then Elasticsearch will do the rest!
news_headlines

49. Click on menu icon, and open Dev Tools.

50. Click on Explore on my own option.

51. Fine tuning precision or recall using
Elasticsearch and Kibana

52. Questions?

53. Join the Elastic Austin User Group for
updates on future workshops!

54. Lisa Jung
https://discuss.elastic.co/