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Quiz on Supervised and Unsupervised Learning

Total questions: 10

Worksheet time: 5mins

Name
Class
Date
1.

What is the primary purpose of a Decision Tree in supervised learning?

a)

To classify data into categories

b)

To predict continuous outcomes

c)

To cluster similar data points

d)

To visualize data in a linear format

2.

Which algorithm is known as one of the best for constructing Decision Trees?

a)

CART

b)

ID3

c)

Support Vector Machine

d)

K-means

3.

What does Entropy measure in the context of Decision Trees?

a)

The amount of uncertainty in data

b)

The accuracy of predictions

c)

The number of decision nodes

d)

The total number of leaves

4.

In a classification tree, what type of variable is the decision variable?

a)

Continuous

b)

Categorical

c)

Ordinal

d)

Nominal

5.

What is the main goal of regression analysis in machine learning?

a)

To visualize data trends

b)

To cluster data points

c)

To predict continuous values

d)

To classify data into distinct categories

6.

Which of the following is a limitation of Decision Trees?

a)

They can handle both categorical and continuous data

b)

They are easy to interpret

c)

They can lead to overfitting

d)

They require less data preprocessing

7.

What is the purpose of Information Gain in Decision Trees?

a)

To measure the effectiveness of a split

b)

To determine the accuracy of the model

c)

To calculate the total number of leaves

d)

To visualize the decision boundaries

8.

Which type of regression is used for predicting binary outcomes?

a)

Linear Regression

b)

Logistic Regression

c)

Polynomial Regression

d)

Support Vector Regression

9.

What is a key characteristic of Unsupervised Learning?

a)

It uses feedback to improve accuracy

b)

It predicts specific outcomes

c)

It finds hidden patterns in data

d)

It requires labeled data

10.

In K-means clustering, what is the first step of the algorithm?

a)

Determine the number of clusters

b)

Assign data points to the nearest centroid

c)

Calculate the mean of each cluster

d)

Initialize the centroids