Lecture 9 Flashcards

1
Q

What is a True-Positive in the confusion matrix?

A

Actual value = True, Predicted value = True.

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2
Q

What is a True-Negative in the confusion matrix?

A

Actual value = False, Predicted value = False.

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3
Q

What is a False-Positive in the confusion matrix?

A

Actual value = False, Predicted value = True.

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4
Q

What is a False-Negative in the confusion matrix?

A

Actual value = False, Predicted value = False.

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5
Q

What does the “Z” stand for in the confusion matrix?

A

Reality/ truth/ the binary parameter of interest.

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6
Q

What does the “T” stand for in the confusion matrix?

A

Test/ judgement.

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7
Q

What is Supervised learning?

A

Building (or training) a model based on a known dependent variable Y.

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8
Q

What is Supervised learning used for?

A

It is used to obtain a numerical score as a base for T+/T-.

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9
Q

How can Supervised learning be done?

A

With a regression-analysis: simple regression, multiple regression, logistic regression.

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10
Q

What is a good solution to create a trade-off between sensitivity and specifity?

A

ROC Curve.

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11
Q

What is a residual?

A

It is the magnitude of error found when comparing the difference between Y and Y’.

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12
Q

What is the advantage of logistic regression?

A

As we compute the probability that Y = 1 based on a subset of independent variables, we may store these values in a new variable and then determine whether Y’ = 0 or Y’ = 1.

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13
Q

How can we choose a suitable cut-off point (Logistic regression)?

A

By plotting an ROC curve and determine T+ and T-.

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14
Q

Where is the Ideal value in the ROC curve?

A

Left Uppercorner.

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15
Q

What are we trying to accomplish in a ROC curve?

A

To minimize the distance between a spot on the line and the upper left corner.

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16
Q

What is the baseline for the AUC (Area Under the Curve)?

A

0.5