data preparation

Question 1

process of preparing data for analysis by
removing or modifying incorrect, incomplete,
irrelevant, duplicated, or improperly formatted data

Answer 1

data cleaning

Question 2

types of data: many different string value

Answer 2

polynomial

Question 3

types of data: exactly 2 values

Answer 3

binomial

Question 4

types of data: a fractional number

Answer 4

real

Question 5

types of data: a whole number

Answer 5

integer

Question 6

types of data: both date and time

Answer 6

data_time

Question 7

rapid miner interface

Answer 7

repository/source tabs
operators/analysis tabs
description tabs
parameter tabs
canvas

Question 8

data will appear in the __ tab

Answer 8

results

Question 9

“read excel” is found in what tab

Answer 9

operator tab

Question 10

to find the basic statistics of each attributes, click __

Answer 10

statistics

Question 11

connect the __ node of the read excel operator and __ of the result knob

Answer 11

out, res

Question 12

filter examples may be found in what tab

Answer 12

operator tab

Question 13

add filter may be found in what tab

Answer 13

parameter tab

Question 14

instead of filtering, you may remove all cases with missing values, using the __ class, instead of add filters

Answer 14

condition

Question 15

replace missing values may be found in what tab

Answer 15

operator tab

Question 16

t/f:in dealing with miscoded data, the out node of the retrieve customer operator must also be connected to the first res of the result knob

Answer 16

f (second knob)

Question 17

__ __ operator may be used to tag the attribute that will be used as the label (target variabls)

Answer 17

set role

Question 18

if two data sets are needed to be merged in order to make an analysis, use the __ operator

Answer 18

join

Question 19

connect the first data set or its result in the _- node of the join operator and the other data set at the right node

Answer 19

left