Read, summarize and describe the approach using a data set different from the one provided in the paper.
computer science
Description
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Exercice 1: (Reading
Assignment)
Applying Naïve Bayes to continuous features using binning.
Tasks:
1.
Read, summarize and describe the approach using
a data set different from the one provided in the paper.
2.
Prepare a 2 to 3 slides presentation explaining
the approach.
Exercice 2:
Given the following data set:
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1.
Create a naive Bayes model for this data set.
2.
What prediction will the naive Bayes model
return for the query q=(1,0,0)?
Exercice 3: (Use of
probabilities)
Mohamed flies frequently and likes to upgrade his seat to first
class. He has determined that, if he
checks in for his flight at least two hours early, the probability that he will
get the upgrade is .75; otherwise, the probability that he will get the upgrade
is .35. With his busy schedule, he checks in at least two hours before his
flight only 40% of the time. Suppose Ali
didn’t receive an upgrade on his most recent attempt. What is the probability
that he arrived late?
Exercice 4:
The task in this question is to create a
naïve Bayes model to monitor a waste water treatment plant. The table below
lists a dataset containing details of activities at a waste water treatment
plant for 13 days. Each day is described in terms of six descriptive features
that are generated from different sensors at the plant. SS-IN measures the
solids coming into the plant per day; SED-IN measures the sediment coming into
the plant per day; COND-IN measures the electrical conductivity of the water
coming into the plant. The features
SS-OUT, SED-OUT, and CONDOUT are the corresponding measurements for the water
flowing out of the plant. The target feature, STATUS, reports the current
situation at the plant:
ok, everything is working correctly; settler,
there is a problem with the plant settler equipment; or solids, there is a
problem with the amount of solids going through the plant.
3.
Create a naive Bayes model that uses probability
density functions to model the descriptive features in this dataset (assume
that all the descriptive features are normally distributed).
4.
What prediction will the naive Bayes model
return for the following query?
SS-IN = 222, SED-IN =
4.5, COND-IN = 1,518, SS-OUT = 74, SED-OUT = 0.25, COND-OUT = 1,642
