Regularization-regression

Regularization-regression

Professional Development

8 Qs

quiz-placeholder

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Regularization-regression

Regularization-regression

Assessment

Quiz

Science

Professional Development

Practice Problem

Medium

Created by

Amjad Khalid

Used 30+ times

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8 questions

Show all answers

1.

MULTIPLE SELECT QUESTION

45 sec • 1 pt

Situations where regularization is useful

Overfitting

Curse of dimensionality

High covariance between variables

Underfitting

2.

MULTIPLE SELECT QUESTION

45 sec • 1 pt

The two most common types of regularization are

Lasso

least square

Σ

Ridge

3.

MULTIPLE SELECT QUESTION

45 sec • 1 pt

The Lasso is particularly useful when you have:

redundant variables

unimportant variables

Underfitting

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The Ridge penalty will

adds the sum of the squared (non-intercept!) values to the loss function

subtract the sum of the squared (non-intercept!) values to the loss function

remove the sum of the squared (non-intercept!) values from the loss function

adds the sum of the absolute values of the coefficients

5.

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Elastic Net is simply a combination of the Lasso and the Ridge regularizations.

False

True

6.

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

The term multicollinearity means

there are low correlations between predictor variables in the model.

there are high correlations between predictor variables in your model.

7.

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

With the Lasso and Ridge it is necessary to ______ the predictor columns before constructing the models

Centralize

standardize

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