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