The Ultimate Unsupervised Learning Quiz: Are You Ready?

1. What is Unsupervised learning?

Training on labeled data

Training on unlabeled data

A type of reinforcement learning

A type of deep learning

Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

Explanation

Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

2. What does the DBSCAN algorithm do in unsupervised learning?

Classify data

Density based clustering algorithm

Predict future data

Generate new data

DBSCAN is a density-based clustering algorithm that works on the assumption that clusters are dense regions in space separated by regions of lower density. It groups 'densely grouped' data points into a single cluster.

Explanation

DBSCAN is a density-based clustering algorithm that works on the assumption that clusters are dense regions in space separated by regions of lower density. It groups 'densely grouped' data points into a single cluster.

3. Which of the following is a type of unsupervised learning?

Regression

Classification

Clustering

Convolutional Neural Network

Clustering, where the algorithm learns to group similar data, is a type of unsupervised learning.

Explanation

Clustering, where the algorithm learns to group similar data, is a type of unsupervised learning.

4. Which algorithm is commonly used for clustering in unsupervised learning?

Naive Bayes

Decision Trees

Random Forests

K-Means

This algorithm tries to minimize the variance of data points within a cluster.

Explanation

This algorithm tries to minimize the variance of data points within a cluster.

5. Which of the following best describes the term 'anomaly detection' in unsupervised learning?

Identifying outliers in data

Identifying the center of data clusters

Classifying data

Generating new data samples

Anomaly detection is the identification of rare events, items, or observations which are suspicious because they differ significantly from standard behaviors or patterns.

Explanation

Anomaly detection is the identification of rare events, items, or observations which are suspicious because they differ significantly from standard behaviors or patterns.

6. Which algorithm is used for hierarchical clustering in unsupervised learning?

Naive Bayes

Decision Trees

Random Forests

Agglomerative clustering

o group the datasets into clusters, it follows the bottom-up approach. It means, this algorithm considers each dataset as a single cluster at the beginning, and then start combining the closest pair of clusters together.

Explanation

o group the datasets into clusters, it follows the bottom-up approach. It means, this algorithm considers each dataset as a single cluster at the beginning, and then start combining the closest pair of clusters together.

7. What is the goal of dimensionality reduction in unsupervised learning?

To reduce computational complexity

To reduce overfitting

To improve data visualization

All of the above

The goal of dimensionality reduction is to reduce the number of dimensions in a way that the new data remains useful.

Explanation

The goal of dimensionality reduction is to reduce the number of dimensions in a way that the new data remains useful.

8. How are the clusters formed in the K-Means algorithm?

Based on nearest neighbors

Based on decision tree splitting

Based on density

Based on distance from centroids

The classification into clusters is done using criteria such as smallest distances, density of data points, graphs, or various statistical distributions.

Explanation

The classification into clusters is done using criteria such as smallest distances, density of data points, graphs, or various statistical distributions.

9. Which of the following is a drawback of K-Means clustering?

It requires the number of clusters to be known.

It can only handle numeric data.

It is sensitive to outliers.

All of the above.

It requires to specify the number of clusters (k) in advance. It can not handle noisy data and outliers. It is not suitable to identify clusters with non-convex shapes.

Explanation

It requires to specify the number of clusters (k) in advance. It can not handle noisy data and outliers. It is not suitable to identify clusters with non-convex shapes.

10. In the context of unsupervised learning, what is an autoencoder used for?

For classification tasks

For clustering tasks

For dimensionality reduction

For time-series prediction

Autoencoders are used to help reduce the noise in data. Through the process of compressing input data, encoding it, and then reconstructing it as an output, autoencoders allow you to reduce dimensionality and focus only on areas of real value.

Explanation

Autoencoders are used to help reduce the noise in data. Through the process of compressing input data, encoding it, and then reconstructing it as an output, autoencoders allow you to reduce dimensionality and focus only on areas of real value.