turning term exam 1

Question 1

data mining is a

Answer 1

process

Question 2

the following stage in data mining involves digging beneath the surface to uncover the structure of the business problem and the data that are available and then match them to one or more data mining tasks for which we may have substantial science and technology to apply

Answer 2

data understanding

Question 3

the following is not an example of machine learning tasks

Answer 3

calculating the annual profit or loss

Question 4

” a computer program is said to learn from experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E

the training data is referred to as

Answer 4

E

Question 5

CRISP-DM is a codification for the data mining process which starts with the following stage

Answer 5

business and data understanding

Question 6

supervised machine learning methods include the following

Answer 6

none of the above

Question 7

business data science requires the combination of the know-how in the following

Answer 7

business domain expertise, mathematics, statistics, computer science

Question 8

the diagram above depicts the following type of machine learning systems

Answer 8

unsupervised

Question 9

the following machine learning algorithm attempts to find associations between entities based on transactions involving them

Answer 9

co-occurrence grouping

Question 10

the following is not mentioned in chapter 1 of the data science for business book

Answer 10

IBM

Question 11

in the diagrams the amount of shading corresponds to

Answer 11

total entropy

Question 12

based on the diagrams which single attribute would you select to spilt between edible and poisonous mushrooms

Answer 12

spore print color

Question 13

entropy is a measure of

Answer 13

purity

Question 14

a basket contains 10 apples and nothing else a bowl contains 5 cherries and nothing else the entropy values of the set of apples in the basket and the set of cherries in a bowl are

Answer 14

0 and 0 respectively

Question 15

a supervised segmentation with tree structured modeling can be done by recursively selecting the best attribute from multiple attributes based on their

Answer 15

information gain

Question 16

in the diagram which are considered as nodes

Answer 16

all of the above (employed; balance and age; class write off and class not write off)

Question 17

the following is an alternative to the entropy measure of information

Answer 17

gini impurity

Question 18

in a decision tree a terminal node is also known as a

Answer 18

leaf

Question 19

the cellular phone churn prediction problem discussed toward the end of chapter 3 uses a historical data set 20000 customer to measure the accuracy of the tree model the authors used a training set consisting of

Answer 19

50% customers who churned and 50% customers who did not churn

Question 20

an instance is also called a

Answer 20

feature vector

Question 21

the objective function of support vector machines is based on the idea that

Answer 21

the wider the bar is between the classes, the better

Question 22

with linear regressions the goal is to find a model that gives the

Answer 22

minimum sum of squared errors

Question 23

a model is a BLANK of reality created to serve a purpose

Answer 23

sampled representation

Question 24

the following describes the parametric learning approach

Answer 24

start by specifying the structure of the model and then continue with calculating the best parameter values given a particular set of training data

Question 25

the above is an example of the BLANK view

Answer 25

instance space

Question 26

the basic linear model is not appropriate for estimating the class probability because the output of the linear function f(x) ranges

Answer 26

from negative infinity to positive infinity

Question 27

the corresponding offs of probability of 0.8 is

Answer 27

4

Question 28

logistic regression models are used widely for

Answer 28

classification

Question 29

support vector machines are used widely for

Answer 29

classification

Question 30

f(x)= w0+w1x1+w2x2+…

in the above general line model the parameters are

Answer 30

w0,w1,w2

Question 31

the variation in mouse size explained by weight divided by the variation in mouse size not explained by weight is called

Answer 31

F

Question 32

BLANK tells us how much of the variation in mouse size can be explained by taking mouse weight into account

Answer 32

R squared

Question 33

in support vector machines to make a threshold that is not so sensitive to outliers we must allow

Answer 33

none of the above

Question 34

there is BLANK variation around the line that we fit by least squares

Answer 34

less

Question 35

the sum of squares of the distance from the mean to each data point is called

Answer 35

SS(mean)

Question 36

R squared is 0.6 which means that BLANK explains 60% of the variation in mouse size

Answer 36

mouse weight

Question 37

the sum of the distances between the data and the line squared is

Answer 37

SS(fit)

Question 38

in support vector machines we use BLANK to determine the number of misclassifications and observations to allow inside of the soft margin to get the best classification

Answer 38

cross validation

Question 39

the variation around the mean can be calculated using

Answer 39

(data-mean)^2 / n

Question 40

in the case of predicting the weight of a mouse based on its size the following value of r2 squared will indicate a perfect prediction

Answer 40

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