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please give me asnwer dont give me method i want solution homework , i need just g , h , i , j , k
please give me asnwer dont give me method i want solution homework i need just ghijkiiiiiii
a Compute P@ and P@ points
b Compute R@ and R@ points
c Provide an example ranking for this query that maximizes P@ points
f Provide an example ranking for this query that maximizes R@ point
d Provide an example ranking for this query that maximizes P@ points
Provide an example ranking for this query that maximizes R@ points
f Provide an example ranking for this query that maximizes @ points
g You have reason to believe that the users of this system will want to examine every
relevant document for a given query. In other words, you have reason to believe that
users want perfect recall. You want to evaluate based on @ Is there a queryspecific
method for setting the value of that would be particularly appropriate in this scenario?
What is it Your answer should be related to RPrecision. points
h Compute average precision AP points
i Provide an example ranking for this query that maximizes average precision AP
points
j Compute DCGie the discounted cumulative gain at rank points
k NDCG is given by NDCG DCGIDCG where IDCG is the DCG
associated with the ideal top ranking associated with this query. points
i What is the ideal top ranking associated with this query notice that the query has
perfect document, excellent document, good documents, fair documents, and the
rest of the documents are bad points
ii IDCG is the DCG associated with the ideal ranking. Compute IDCGHint:
compute DCG for your ranking proposed in part i points
iii Compute NDCG using the formula above. : PrecisionRecall Curves points
A PrecisionRecall PR curve expresses precision as a function of recall. Usually, a PR
curve is computed for each query in the evaluation set and then averaged. For simplicity,
the goal in this question is to draw a PRcurve for a single query. Draw the PRcurve
associated with the ranking above same query, same results Hint: Your PR curve
should always go down with increasing levels of recall interpolation points
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