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ML Quiz 10

Authored by Anik Chowdhury

Computers

12th Grade - Professional Development

Used 7+ times

ML Quiz 10
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11 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

__________ is a machine learning technique that iteratively combines a set of simple and not very accurate classifiers (referred to as "weak" classifiers) into a classifier with high accuracy (a "strong" classifier) by upweighting the examples that the model is currently misclassfying.

Binning

Boosting

Bagging

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

_________ is a synthetic feature that encodes nonlinearity in the feature space by multiplying two or more input features together.

candidate sampling

feature cross

bucketing

scaling

3.

MULTIPLE SELECT QUESTION

45 sec • 1 pt

Features created by ______ or ______ alone are not considered synthetic features.

normalizing

bagging

scaling

boosting

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In practice, machine learning models frequently cross continuous features.

True

False

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Different cities in California have markedly different housing prices. Suppose you must create a model to predict housing prices. Which of the following sets of features or feature crosses could learn city-specific relationships between roomsPerPerson and housing price?

One feature cross: [binned latitude X binned longitude X binned roomsPerPerson]

Two feature crosses: [binned latitude X binned roomsPerPerson] and [binned longitude X binned roomsPerPerson]

One feature cross: [latitude X longitude X roomsPerPerson]

Three separate binned features: [binned latitude], [binned longitude], [binned roomsPerPerson]

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

If training loss gradually decreases, but validation loss eventually goes up. In other words, this generalization curve shows that the model is ______

Underfitting

Overfitting

None

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

If your lambda value is too high, your model will be simple, but you run the risk of overfitting your data.

True

False

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