Notebook 2: Association Rule Mining

Question 1

Iterable

Answer 1

an object that can be interated over (like in a for loop)
ex: lists, tuples, sets, strings, dictionaries, ranges

-needs to implement __iter__()
-You can pass an iterable to functions like list(), tuple(), and for loops because they can be converted to an iterator.

Question 2

iterator

Answer 2

An object used to perform the actual iteration.
-Implements both __iter__() (returns itself) and __next__() (returns the next value in the sequence or raises StopIteration when done).
-Iterators are stateful, meaning they remember their current position in the sequence.
-it’s a one time thing, need a new iterator once exhasted

Question 3

default dictionary

Answer 3

-requires a factory function to add a key when it does not exist (could be built in type like into or list, custom function)
-available from collections.defaultdict
-Python’s built-in dictionaries, you always to have to check whether a key exists before updating it

Question 4

itertools.permutations(iterable, r=None)

Answer 4

returns all possible ordered arrangements of r elements from the iterable

Question 5

itertools.combinations(iterable, r)

Answer 5

Generates all possible unordered combinations of r elements from the iterable.

Question 6

association rule mining - confidence formula

Answer 6

confidence a -> b = support(A U B) / support (A)

Question 7

association rule mining - support

Answer 7

Support is how often an item (or itemset) shows up in the data

frequency or prevalence of an itemset (or items) in the dataset

Question 8

association rule mining - itemset

Answer 8

individual items in itemset

Question 9

association rule mining - pair count

Answer 9

items in the itemset that appear together

Question 10

dictionary comprehension formatting

Answer 10

{key_expression: value_expression for item in iterable if condition}

Question 11

range

Answer 11

range(start, stop, step)

Answer 12

XOR or exlusive or

Question 13

**

Answer 13

exponentiation