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Machine Learning quiz 2

Total questions: 12

Worksheet time: 12mins

Name
Class
Date
1.

Which of the following is used for unsupervised learning task?

a)

Gaussian Mixture model

b)

K-Means

c)

ID3

d)

Multiplayer Perception

2.

Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

a)

Decision tree

b)

Regression

c)

Classification

d)

Random forest

3.

To find the minimum or maximum of a function, we set the gradient to zero because;

a)

The value of a function at extrema is always zero

b)

Depends on the type of problem

c)

Both A and B

d)

None of the above

4.

The most widely used tools and metrics to assess a classification model are:

a)

Confusion matrix

b)

Cost-sensitive accuracy

c)

Area under the ROC curve

d)

All of the above

5.

Which of the following is a good test dataset charateristic?

a)

Large enough to yield meaningful results

b)

Is a representative of the dataset as a whole

c)

Both A and B

d)

None of the above

6.

Which of the following is a disadvantage of decision trees?

a)

Factor analysis

b)

Decision trees are robust to outliers

c)

Decision trees are prone to overfit

d)

None of the above

7.

How do you handle missing or corrupted data in a dataset?

a)

Drop missing rows or collumns

b)

Replace missing values with Mean/Median/Mode

c)

Assign a unique category to the missing values

d)

All of the above

8.

What is the purpose of performing cross-validation?

a)

To access the predictive performance of the models

b)

To judge how the trained model performs outside the sample on test data.

c)

Both A and B

9.

Why is second order differencing in time series needed?

a)

To remove stationarity

b)

To find the maxima or minima at the local point

c)

Both A and B

d)

None of the above

10.

When performing regression or classification, which of the following is the correct way to preprocess data?

a)

Normalize the data -> PCA -> Training

b)

PCA -> Normalize PCA output -> Training

c)

Normalize the data -> PCA -> Normalize PCA output -> Training

d)

None of the above

11.

Which of the following is an example of feature extraction?

a)

Constructing bag of words vector from an email

b)

Applying PCA projects to a large high-dimensional data

c)

Removing stopwords in a sentence

d)

All of the above

12.

What is pca.components_ in sklearn?

a)

Set of all eigen vectors for the projection space

b)

Matrix of principal components

c)

Result of the multiplication matrix

d)

None of the above