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Wednesday, 30 December 2020

Machine Learning Multiple Choice Questions and Answers 24

Top 5 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, classification, ridge regression, lasso regression, statistics


Machine learning Quiz Questions - Set 24

1. The classifier’s behavior is determined by the coefficients. These coefficients are usually referred as ________.

a) Weights

b) Tasks

c) Values

d) Behaviors

Answer: (a) Weights

The classifier’s behavior is determined by the coefficients, wi.These are usually called weights.

 

2. Null and alternative hypotheses are statements about:

a) population parameters.

b) sample parameters.

c) sample statistics.

d) it depends - sometimes population parameters and sometimes sample statistics.

Answer: (a) Population parameters

The null and alternative hypotheses are two mutually exclusive statements about a population. A hypothesis test uses sample data to determine whether to reject the null hypothesis.

Null hypothesis (H0) - The null hypothesis states that a population parameter (such as the mean, the standard deviation, and so on) is equal to a hypothesized value.

Alternative Hypothesis (H1) - The alternative hypothesis states that a population parameter is smaller, greater, or different than the hypothesized value in the null hypothesis.[Refer for more]

 

3. In hypothesis testing, a Type 2 error occurs when

a) The null hypothesis is not rejected when the null hypothesis is true.

b) The null hypothesis is rejected when the null hypothesis is true.

c) The null hypothesis is not rejected when the alternative hypothesis is true.

d) The null hypothesis is rejected when the alternative hypothesis is true.

Answer: (c) The null hypothesis is not rejected when the alternative hypothesis is true

Type 2 error is caused when the null hypothesis is false and we fail to reject it.

 

4. What type of penalty is used on regression weights in Ridge regression?

a) L0

b) L1

c) L2

d) None of the above

Answer: (c) L2

Ridge regression adds “squared magnitude” of coefficient as penalty term to the loss function. L2 regularization adds an L2 penalty, which equals the square of the magnitude of coefficients.

Ridge regression shrinks the regression coefficients, so that variables, with minor contribution to the outcome, have their coefficients close to zero.

The shrinkage of the coefficients is achieved by penalizing the regression model with a penalty term called L2-norm, which is the sum of the squared coefficients. L2 regularization is used to avoid overfitting of data.

When do we use L2 regularization?

L2 regularization is best used in non-sparse outputs, when no feature selection needs to be done, or if you need to predict a continuous output.

 

5. Which of the following of the coefficient is added as the penalty term to the loss function in Lasso regression?

a) Squared magnitude

b) Absolute value of magnitude

c) Number of non-zero entries

d) None of the above

Answer: (b) Absolute value of magnitude

Lasso regression adds “absolute value of magnitude” of coefficient as penalty term to the loss function.

Lasso regression shrinks the regression coefficients toward zero by penalizing the regression model with a penalty term called L1-norm, which is the sum of the absolute coefficients.

 

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Related links:

Multiple choice quiz questions in machine learning

Differentiate between Lasso and ridge regression

When do we need L2 regularization technique

Define Type 2 error

What does null and alternative hypotheses state about population parameters


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