Parameters and parametric models

Parameters and parametric models

University

20 Qs

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Parameters and parametric models

Parameters and parametric models

Assessment

Quiz

Computers

University

Medium

Created by

Emily Anne

Used 1+ times

FREE Resource

20 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the max_iter parameter in logistic regression specify?

The number of features in the model

The maximum number of iterations the optimization algorithm can run

The number of coefficients in the model

The minimum convergence threshold

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following describes the purpose of the solver parameter in logistic regression?

It determines the convergence threshold for training

It specifies the optimization algorithm used to find model coefficients

It decides the size of the dataset to process

It sets the data normalization strategy

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the multi_class='auto' parameter do in logistic regression?

Forces binary classification regardless of the dataset

Applies one-vs-rest classification for all solvers

Chooses the best strategy for handling multiple classes based on the solver

Converts all target variables to numeric values

4.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

What is an example of sparse data? (Select all that apply)

A dataset where most of the values are zeros or missing

A dataset with evenly distributed numerical data

A binary image representation where most pixels are black

A dataset with dense matrix representation

5.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Which of the following is true about L1 regularization (Lasso)? (Select all that apply)

It shrinks coefficients closer to zero but never exactly zero

It adds a penalty proportional to the absolute values of the model coefficients

It forces some coefficients to become exactly zero

It uses the sum of squared coefficients as a penalty

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

When should L1 regularization (Lasso) be preferred over L2 regularization (Ridge)?

When feature selection is required

When all predictors are important

When preventing overfitting is not necessary

When we want to use all features equally

7.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Why is regularization important in machine learning? (Choose 2)

It allows the model to learn 'noise' from the data

It makes the model simpler and easier to interpret

It prevents overfitting

It ensures the training data is perfectly memorized

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