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Flashcards in Analytic Techniques Deck (110)
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61

what is this for? and what does it mean?
Hcredit = -(0.7 log2(0.7) + 0.3log2(0.3)) = 0.88 ( very close to 1)

base entropy (decision tree)
high entropy

62

what does conditional entropy do in decision trees?

attribute values give more information about the class membership

63

what is information gain?

difference between base and conditional entropy

64

if you have a high information gain what does than mean?

first variable for tree split

65

which classifier for these questions:
do I want class probabilities or just class labels

logistic regression
decision tree

66

which classifier for these questions:
do I want insight into how the variables affect the model?

logistic regression
decision tree

67

which classifier for these questions:
is the problem high dimensional?

naive bayes

68

which classifier for these questions:
do I suspect some of the inputs are correlated?

decision trees
logistic regression

69

which classifier for these questions:
do I suspect sone if the inputs are irrelevant?

decision tree
naive bayes

70

which classifier for these questions:
are there categorical variables with a large number of levels?

naive bayes
decision tree

71

which classifier for these questions:
are there mixed variable types?

decision tree
logistic regression

72

which classifier for these questions:
are there non-linear elements or discontinuities in the data?

decision tree

73

what is time series analysis?

equally spaced out values over time

74

what does time series analysis do?

forecast

75

what is the difference between univariate time series and multivariable time series?

uni is one variable

76

in time series what is the box-jerkins method?

predicts the future

77

what does ARMA stand for?

autoregressive moving averages

78

who invented ARMA model?

box-jenkins

79

what does the box-jenkins method assume the random component is?

stationary sequence

80

what does a stationary sequence mean?
a)constant variance
b)autocorrelation does not change
c)constant deviance
d) constant mean

constant variance
autocorrelation does not change
constant mean

81

to obtain a stationary sequence the data must be?

de-trended
seasonally adjusted

82

what does the ARIMA model do?

uses method differencing to render the data stationary

83

how do you remove a simple linear trend in time series?

subtracting least-squares-fit straight line

84

how do you do a seasonal adjustment for time series?

calculating the average for each month and subtracting them from the actual value

85

what model uses P,Q in time series?

ARMA

86

in AR what is Y?
a)Yt is a linear combination of its last p values
b)Yt is a linear combination of its last q values

a)Yt is a linear combination of its last p values

87

in MA what is Y?
a)Yt is a constant value plus the effects of a dampened white noise process over the last p time values (lags)
b)Yt is a constant value plus the effects of a dampened white noise process over the last q time values (lags)

b)Yt is a constant value plus the effects of a dampened white noise process over the last q time values (lags)

88

What is the d in ARIMA (p,d,q)?

differencing term

89

what does ARIMA stand for?

autoregressive integrated moving average

90

what does p mean in time series (ARMA, ARIMA)?

number of autoregressive terms