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Data Analytics > Chapter 8 > Flashcards

Chapter 8 Flashcards

1
Q

supervised learning(classification)

A 
the training data (observations, measurments, etc.) are accompanied by labels indicating the class of  the observations
new data is classified based on the training set
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2
Q

Unsupervised learning (clusterng)

A 
THe class labels of training data is unknown
Given a set of measurments, observations, etc. with the aim of establishing the existence of classes or clusters in the data.
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3
Q

Classification

A 
predicts categorical calss labesl (discrete or nominal)
Classifies data based on the training set and the vaslues (class labels) in a classifying attribute and uses it in classifying new data
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4
Q

Numeric prediction

A 
models continous-valued functions, i.e., predicts unknown or missing values
credit loan aproval
medical diagnosis
fruad detection
web page categorization
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5
Q

Learning step (model construction)

A

describing a ser of predetermined classes
each tuple/sample is assumed to belong to a predefined class, as determined by the class ;abe; attribute
the setr of tuples ued for model construction is the traning set

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6
Q

Classification step (model usage)

A

for classifying future or unknown objects

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7
Q

Estimate accuracy

A

the known label of test sample is compared with the classified reult from the model

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8
Q

acurracy rate

A

is the percentage of test set samples thgat are correctly classified by the model

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9
Q

test set

A

independent of traning set (otherwise overfitting)

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10
Q

ckassify new data

A

if the accuracy is acceptable

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11
Q

validation test set

A

if the test set is used to select modeles

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12
Q

decision tree induction

A

the learning of decison trees form class labled training tuples

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13
Q

internal node

A

denotes a test on an attriubute

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14
Q

branch

A

represents an outcome of the test

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15
Q

leaf node or terminal node

A

holds a class label

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16
Q

root node

A

top most node in a tree

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17
Q

d-data partition

A

initially it is the complete ser of training tuples and their associated class labels

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18
Q

attribute-list

A

list ofattrivutes describes the tuples

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19
Q

attribute_selection_method

A

a heuristic procedure for selecting the attribute that best disctiminates the given tuples according to class

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20
Q

gini index

A

enforce the resulting tree to be binary

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21
Q

information cange

A

allows multiway splits

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22
Q

determine splitting criterion

A

idelly the resulting partitions at each branch are as pure as possible

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23
Q

pure partition

A

when all the tuples in it belong to the same class

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24
Q

discrete valued partitioning

A

each variable has a partition or line to it

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25
Q

continous valued partition

A

there is a slit point resaulting in two partitions either below or over the split

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26
Q

discrete-valued and a binary tree must be produced

A

A question is asked and the outcome is the partition or line

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27
Q

attribute selection measures

A

splitting rules

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28
Q

The splitting attribute

A

the attribute habving the best score fot the measure, either maxiamise or minimize, is chosen for the given tuple.

29
Q

entropy

A

is the state of disorder, confusion, surprise. uncertainty, and disorganization ( second law of thermal dynamics, ethropy increases over time)

30
Q

The more heterogenous the event the more uncertainty

A

less heterogeneous, more homogenous thge event, less uncertainty

31
Q

x-axis

A

the probability of the event

32
Q

y-axis

A

is the heterogeneity or the imurity denoted by H(X) - Entropy (impuraty measure)

33
Q

uncertantiy chjanges depending on the likelihood of an event

A

pr(x=0) no uncertainty, pr(x=0.0) maximum uncerainty, pr(x=1) no uncertainty

34
Q

higher entropy

A

higher uncertainty

35
Q

lower entropy

A

lower uncertainty

36
Q

info(D)

A

is just the average amount of infromation needed to identify the class label of a tuple in D

37
Q

High ingformation gain

A

minimizes the information needed to classify truple in the resulting partitions, and reflects the least randomness or impurity in the partitions

38
Q

information gained

A

defined as the differnce between the priginal information required ( based on just the proportion of classes) and the new requirement (obtained after partitioning on A)

39
Q

Gain(a)

A

tells us how much would be ained by brachinbg on A. it is the expected reduction in the information requirement caused by knowing the value of A. THe attribute A with the highest information gain is chosen as the splitting attribute node N

40
Q

gain ratio for atrribute selection (c4.5)

A

information gain meaure is biased towars atrributes with a large number of values. IT uses gain ratio to overcome the problem (normalization to information gain)

41
Q

maximum gain ratio

A

the attribute is slected as the splitting attribute

42
Q

gini index measure the impuritty of D

A

if a data set D contins examples form n classes =, giuni index , gini(d) is define
The gini index considers a binary split for each attribute

43
Q

Smallest gini(D)

A

the larges reduction in impuity is chosen to split the node

44
Q

information gain

A

biased towards multivaalued attributes ( attributes with a large nu,ber of values)

45
Q

Gain ratio

A

tends to prefer unbalanced splits in which one p[partition is much smaller than the others

46
Q

gigni index

A

biased to multivalued attributes
has difficulty when number of classes is large
ttends to favor test that result in equal-sized portions and purity in both partitions

47
Q

evaluation metrics

A

how can we measure accuracy? other metrics to consider?

48
Q

postive tuples samples

A

tuples of the main class of interest

49
Q

negative tuples

A

all other tuples

50
Q

true positve

A

the postitve tuples that were correctly labled by the classsifier

51
Q

ture negatives

A

the negative tuples that were correctly labled by the classifier

52
Q

false positve

A

the negaive tuples that were incorrectyl labled as psoitive

53
Q

false negative

A

the poistive tiuples that were mislabled as negaive

54
Q

ressubstitution error

A

error rate on training set insted of a test set

55
Q

classifier accuracy or recognition rate

A

percentage of test set tuples that are correctly classified (TP + TN)/All

56
Q

error rate

A

misclassification rate; 1-accuracy

(fp+fn)/All

57
Q

Class imbalance problem

A 
one calss may be rare, e.g. fraud or cancer
significant majority of the negative class and minority of the positive class
58
Q

sensitivity

A

true pisitve recognition rate; tp/p

59
Q

specifitity

A

true negatice recognition rate; tn/n

60
Q

precision

A

exactness, what % of tuples that the classifier labeled as positive are actually psitive

61
Q

recall

A

completeness, what % of positive tuples did the classifier label as positive, the perfect score is 1.0

62
Q

perfect percision score

A

1.0 for a class C means that every tuple that the classifier leaves as belonging to class C does indeed being to class C. it does not tell us anything about the number of class C tuples that the classifier mislabled

63
Q

perfect recall score

A

1.0 for C means that very item from class C was labeled as such, but ut does not tell us how many thoeth tuples were incorrectly labeled as belonging to class C.

64
Q

f measeure

A

combine precision and recall in one formula, harmonic mean of precision and recall gives equal weight to ptrecision and recall

65
Q

accuracy

A

classifier accuracy, predicting class label

66
Q

speed

A

time to sonstruct the model, taining time

time to use the model. classification or prediction time

67
Q

robustness

A

handling noise and missing values

68
Q

scalability

A

efficieny in disk resident database

69
Q

interpretability

A

undersatading and insight provided by the model

Data Analytics (5 decks)

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