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Worksheets

Machine Learning Types

Total questions: 18

Worksheet time: 9mins

Name
Class
Date
1.

What is the primary goal of supervised learning?

a)

To maximize rewards through trial and error

b)

To predict output based on labeled training data

c)

To group similar objects into clusters

d)

To find hidden patterns in unlabeled data

2.

Which of the following is an example of regression in supervised learning?

a)

Predicting the price of a house

b)

Segmenting customers into groups

c)

Classifying emails as spam or not spam

d)

Finding relationships between items in a dataset

3.

In supervised learning, what does the output variable Y represent?

a)

The input data

b)

The mapping function

c)

The predicted output

d)

The training data

4.

What type of classification involves predicting more than one class for each input?

a)

Multi-label classification

b)

Imbalanced classification

c)

Multi-class classification

d)

Binary classification

5.

Which algorithm is commonly used for clustering in unsupervised learning?

a)

Logistic Regression

b)

Random Forest

c)

K-Means

d)

Support Vector Machine

6.

What is the main characteristic of reinforcement learning?

a)

Learning by clustering data

b)

Learning through trial and error

c)

Learning without supervision

d)

Learning from labeled data

7.

Which of the following is NOT a type of regression?

a)

Non-linear Regression

b)

Simple Regression

c)

Multiple Regression

d)

Hierarchical Regression

8.

What is the purpose of dimensionality reduction in unsupervised learning?

a)

To simplify the dataset by reducing the number of variables

b)

To increase the number of features

c)

To classify data into categories

d)

To predict continuous outcomes

9.

In the context of classification, what does 'imbalanced classification' refer to?

a)

Having equal representation of all classes

b)

Having one class with significantly more examples than others

c)

Classifying data into multiple labels

d)

Using algorithms that balance the classes

10.

Which of the following algorithms is used for association rule learning?

a)

A-priori

b)

K-Means

c)

Linear Regression

d)

Decision Trees

11.

What is the main focus of clustering algorithms?

a)

To predict numerical values

b)

To maximize rewards

c)

To group similar objects together

d)

To classify data into predefined categories

12.

What is the output of a binary classification task?

a)

Clusters of similar data

b)

Continuous values

c)

Multiple classes

d)

Two mutually exclusive categories

13.

Which of the following is a characteristic of supervised learning?

a)

No labeled data is used

b)

Labeled data is provided for training

c)

It focuses on finding hidden patterns

d)

The model learns from feedback

14.

What is the goal of regression analysis?

a)

To classify data into categories

b)

To predict a continuous numerical output

c)

To group similar data points

d)

To find relationships between categorical variables

15.

Which of the following is an example of a reinforcement learning application?

a)

Customer segmentation

b)

Autonomous vehicles

c)

Weather forecasting

d)

Spam detection

16.

What does the term 'multi-class classification' refer to?

a)

Classifying data into two categories

b)

Classifying data with overlapping labels

c)

Classifying data into more than two categories

d)

Classifying data with no labels

17.

Which of the following is a common use case for unsupervised learning?

a)

Spam detection

b)

Predicting house prices

c)

Email classification

d)

Customer segmentation

18.

What is the main objective of using algorithms like XGBoost in regression?

a)

To find associations between items

b)

To cluster similar data points

c)

To predict continuous outcomes

d)

To classify data into categories