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ISYE 6501 Study Guide Questions with Verified Correct Answers

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ISYE 6501 Study Guide Questions with Verified Correct Answers

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ISYE 6501 Study Guide Questions with
Verified Correct Answers
What does SVM stand for?

Support Vector Machine

Is written text structured or unstructured?

Unstructured

When we increase the sum of the square of the coefficients we...

Decrease the distance between the lines

In SVM soft classifier we tradeoff between maximizing ___ and minimizing ___

margin and errors

If lambda gets small what gets emphasized, large margin or minimizing training error?,

Minimizing errors.

What is a support vector?

A point that holds up a shape.

Does ...[⅔(a-1)+1/3(a+1)] move an SVM classifier up or down?

Up

How do you make errors more costly in a soft SVM classifier?

include a multiplier for the point-error term.

If an SVM coefficient is very close to zero...

that term is not very important to the classification.

,What is the difference between standardization and scaling?

Scaling is bounded in range. Standardization is scaling to a normal distribution.

Standardization is the (value - factor mean) / (factor standard deviation)

What is the 2-norm?

Euclidean distance

What is the 1-norm?

The rectilinear (Manhattan) distance

What is the infinity norm?

The value of the largest dimension

Measuring the quality of a model is called?

Validation

What does a confusion matrix show?

The performance of a classification model.

A time series outlier that seems "off the curve" is called a...

contextual outlier.

A data element that is different from all other data in a set is called a...

point outlier.

When something is missing in a range of points

it is called a..., collective outlier.

The whiskers on a box plot extend to...

,the 10th and 90th percentiles (or 5th and 95th)

Why are hypothesis tests generally not sufficient for change detection?

They are slow to detect changes.

In CUSUM, T is _____ and C is _____.,

Threshold and a "bring down factor"

In a CUSUM model, you adjust T and C to manage the tradeoff between...,

early detection and false-alarms

In exponential smoothing, if the data is less random, then you want to pick an alpha

that is...,

Close to 1.

What is the initial condition for T in exponential smoothing with trending?

T_i=0

In cyclic exponential smoothing, L represents...,

The length of the cycle or season

In cyclic exponential smoothing, C_1 ... C_L = ___?,

1. In other words, initialize it to no initial cycle.

Exponential, trending and cyclic smoothing are also referred to as

single double and triple.

Triple smoothing is also known as?

Winter's or Holt-Winter's

, What is the optimization formula for Exponential Smoothing?

min(F_t-Xt)^2 where alpha and beta are between 0 and 1.

ARIMA stands for?

Autoregressive Integrated Moving Average

Exponential smoothing is an order ___ autoregressive model.

Infinity. It uses data going all the way back.

For ARIMA, the D parameter is used to specify ___.

, The order, or the differences of the differences of the differences (d-times.)

For ARIMA, the P parameters is used to specify ____.,

The order of periods (autoregression).

For ARIMA, the Q parameter is used to specify ______.,

The order of the moving average.

ARIMA(0,1,1) is ?,

Exponential smoothing.

What is the order of the ARIMA parameters?

pdq

GARCH estimates what?

Variance.

Variance can be a proxy for ___ or ___.

volatility or Risk

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