Unsupervised Machine Learning - Crash Course Statistics

The video explores unsupervised machine learning, focusing on clustering techniques like K-means and hierarchical clustering. It explains how these methods can group data without predefined labels, using examples like pizza customer segmentation and Autism Spectrum Disorder analysis. The video also discusses evaluating clusters with silhouette scores and highlights the practical benefits of clustering in various fields.

What is the main difference between supervised and unsupervised machine learning?

• Both use labeled data but in different ways.

• Supervised learning uses labeled data, while unsupervised learning does not.

• Neither uses labeled data.

• Unsupervised learning uses labeled data, while supervised learning does not.

In the K-means clustering example, what is the role of centroids?

• They are the final clusters.

• They are the initial random points used to form clusters.

• They are the data points farthest from the clusters.

• They are the labels for the data points.

How does the K-means algorithm determine when to stop iterating?

• When the centroids and groups stop changing.

• When the number of clusters reaches a predefined limit.

• When the silhouette score is maximized.

• When all data points are assigned to a single cluster.

What does a high silhouette score indicate about a cluster?

• The cluster is well-separated from other clusters.

• The cluster has a lot of internal variation.

• The cluster is very similar to other clusters.

• The cluster has a large number of data points.

Which of the following is an example of a low silhouette score scenario?

• Clustering various species of birds based on wing span.

• Clustering different types of fruits based on color.

• Clustering lollipops and filet mignon based on sugar content.

• Clustering filet mignon and New York strip steak based on protein content.

What is a dendrogram used for in hierarchical clustering?

• To determine the number of clusters needed.

• To calculate the silhouette score.

• To show the hierarchical relationship between clusters.

• To visualize the final clusters.

How does hierarchical clustering differ from K-means clustering?

• K-means clustering creates a hierarchy of clusters.

• K-means clustering is used for time-series data.

• Hierarchical clustering requires labeled data.

• Hierarchical clustering merges clusters based on similarity.

What is the purpose of creating clusters in the context of Autism Spectrum Disorder (ASD)?

• To eliminate the need for therapy.

• To classify all individuals as high functioning.

• To diagnose new disorders.

• To provide more targeted and effective treatments.

What is the main advantage of using unsupervised machine learning for creating groups?

• It is faster than supervised learning.

• It always provides accurate predictions.

• It requires less computational power.

• It helps in decision-making without predefined labels.

Which of the following is NOT a benefit of clustering in unsupervised learning?

• Improving targeted marketing strategies.

• Enhancing medical treatment plans.

• Increasing the number of data points.

• Providing better product recommendations.