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Season 5 #Spaic Machine learning Weekly Quiz

Total questions: 20

Worksheet time: 11mins

Name
Class
Date
1.

Let's say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data. You want to apply one hot encoding (OHE) on the categorical feature(s). What are the challenges you may face?

a)

Frequency distribution of categories is different in train as compared to the test dataset.

b)

Train and Test always have same distribution.

c)

Other challenges

2.

What are support vectors?

a)

Output vectors that are Closest to the hyperplane

b)

Output vectors that are farthest from the hyperplane

c)

Input vectors that are farthest from the hyperplane

d)

Input vectors that are closest to the hyperplane

3.

Select the application of decision trees in machine learning

a)

Unsupervised learning

b)

Infinite attributes

c)

Flattening nodes

d)

Classification

4.

What are association rules?

a)

Correlations and reliability between variables in a dataset

b)

Correlations and relationships between variables in a dataset

c)

Assumptions and mentorships between variables in a dataset

d)

Aggregations and relationships between variables in a dataset

5.

In convolutional neural networks, what does pooling reduce?

a)

The number of parameters in a network

b)

How few activations to implement

c)

The number of max pixels

d)

Insertions between convolutional layers

6.

What is a Signal?

a)

An overly complex model

b)

A random noise in the data

c)

Data gathering

d)

The target patterns that you want to learn

7.

Which one of these isn't an indicator of a models quality?

a)

Consistency

b)

Feature engineering

c)

Size of a dataset

d)

Precision and recall

8.

What is computed by MSE in machine learning?

a)

Classification

b)

Entropy

c)

Bias

d)

Error

9.

What is feature bagging?

a)

A random subset of features for each subtree

b)

Everything you want it to be

c)

Nothing you want it to be

d)

A deterministic subset of features for each subtree

10.

What represent the values that will flow through the graph, in computational graph under Tensorflow?

a)

Tuple

b)

ops

c)

Session

d)

None of these

11.

The assets library does not contain which of the following options?

a)

Datasets

b)

Pipelines

c)

Modules

d)

Models

12.

In how many ways data can be imported using the Import Data module?

a)

3

b)

1

c)

2

13.

Which of the following data types is not available while previewing a schema?

a)

Char

b)

Boolean

c)

Date

d)

Decimal

14.

Which one in the following is not Machine Learning disciplines?

a)

Information Theory

b)

Neurostatistics

c)

Optimization + Control

d)

Physics

15.

What kind of learning algorithm for "Facial identities or facial expressions"?

a)

Recognizing Anomalies

b)

Prediction

c)

Generating Patterns

d)

Recognition Patterns

16.

Estimate the price of a house

a)

Cluster

b)

Class

c)

Regression

17.

Select approaches for scaling data

a)

Standardization

b)

Noise removal

c)

Normalization

d)

Data encoding

18.

Let’s say we have an image of size 45x55. It is a grayscale image. How many pixels do we need to represent this image in a numerical format?

a)

2240

b)

2420

c)

2152

d)

2340

19.

Identify parametric machine learning algorithms

a)

Linear regression

b)

CNN

c)

Logistic regression

d)

Naïve Bayes

20.

Rescales the data into the range [0, 1]

a)

Standardization

b)

Normalization