Quizizz-Understanding Unsupervised Learning

What is the main goal of unsupervised learning?

• To predict outcomes based on labeled data

• To uncover patterns and structures in unlabeled data

• To fine-tune a model given labeled data

• To evaluate model performance using test data

Answer explanation

The main goal of unsupervised learning is to uncover patterns and structures in unlabeled data, unlike supervised learning, which relies on labeled data to predict outcomes.

Which of the following is a common clustering algorithm?

• Support Vector Machine

• K-means clustering

• Linear Regression

• Random Forest

Answer explanation

K-means clustering is a widely used clustering algorithm that partitions data into K distinct clusters based on feature similarity. The other options, like Support Vector Machine and Linear Regression, are not clustering algorithms.

In anomaly detection, which of the following tasks would be an example?

• Categorizing emails into spam or not spam

• Detecting fraud in financial transactions

• Recommending products based on user behavior

• Predicting house prices based on features

Answer explanation

Detecting fraud in financial transactions is a clear example of anomaly detection, as it involves identifying unusual patterns that deviate from normal behavior, unlike the other options which focus on classification or prediction.

Dimensionality reduction is primarily used for which of the following purposes?

• To make data easier to manage and visualize

• To increase the number of features in a dataset

• To classify data into different categories

• To evaluate model performance on labeled data

Answer explanation

Dimensionality reduction simplifies data by reducing the number of features, making it easier to manage and visualize. This is crucial for understanding complex datasets, unlike the other options which do not align with its primary purpose.

What is the primary application of association mining?

• Generating new data samples

• Identifying rare events in a dataset

• Discovering relationships between data points

• Reducing the number of features in a dataset

Answer explanation

The primary application of association mining is discovering relationships between data points, which helps in understanding patterns and correlations within datasets.

True or False: Generative modeling can be used to create new synthetic data that resembles the training data.

• True

• False

Answer explanation

True. Generative modeling techniques, such as GANs and VAEs, are designed to learn the underlying distribution of training data and can generate new synthetic data that closely resembles it.

Which unsupervised learning application involves segmenting images into meaningful regions?

• Topic Modeling

• Clustering for Image Segmentation

• Density Estimation

• Association Mining

Answer explanation

Clustering for Image Segmentation is an unsupervised learning technique that groups pixels in an image into meaningful regions, making it the correct choice for segmenting images.

True or False: Topic modeling helps in identifying themes in large collections of text data.

• True

• False

Answer explanation

True. Topic modeling is a technique used to analyze large collections of text data, helping to identify underlying themes and patterns by grouping similar words and phrases together.

Density estimation is useful for which of the following tasks?

• Predicting future sales

• Estimating the probability density of data

• Classifying images

• Clustering similar images together

Answer explanation

Density estimation is primarily used for estimating the probability density of data, allowing us to understand the distribution of data points. The other tasks listed involve different techniques.

What type of unsupervised learning is typically used in market basket analysis?

• Clustering

• Dimensionality Reduction

• Association Mining

• Anomaly Detection

Answer explanation

Association Mining is the correct choice for market basket analysis as it identifies patterns and relationships between items purchased together, helping businesses understand customer buying behavior.