Podcast
Questions and Answers
How does unsupervised learning differ from supervised learning in terms of the data provided to the algorithm?
How does unsupervised learning differ from supervised learning in terms of the data provided to the algorithm?
- There is no difference, both supervised and unsupervised learning algorithms require labeled data.
- Unsupervised learning algorithms are provided with explicit examples of correct answers, unlike supervised learning.
- Supervised learning requires labeled data examples, while unsupervised learning does not. (correct)
- Unsupervised learning requires labeled data, while supervised learning does not.
In unsupervised learning, after the algorithm presents a structure for review, what is the nature of the subsequent process?
In unsupervised learning, after the algorithm presents a structure for review, what is the nature of the subsequent process?
- A one-time evaluation to validate initial findings.
- It's a highly iterative process aimed at discovering meaningful patterns and relationships. (correct)
- A direct implementation of the algorithm's findings without further analysis.
- A process focused on discarding outliers to refine the initial structure.
How is the effectiveness of unsupervised learning typically evaluated, given the absence of direct metrics?
How is the effectiveness of unsupervised learning typically evaluated, given the absence of direct metrics?
- By calculating the accuracy of the discovered patterns against a predefined standard.
- By analyzing the informativeness of data visualization and the discovery of subgroups within the data. (correct)
- By assessing the algorithm's ability to minimize errors during the learning process.
- By measuring the computational efficiency and speed of the learning algorithm.
What role does human supervision play in unsupervised learning?
What role does human supervision play in unsupervised learning?
Which task exemplifies the application of unsupervised learning?
Which task exemplifies the application of unsupervised learning?
What is another term for clustering in the context of unsupervised learning?
What is another term for clustering in the context of unsupervised learning?
In clustering, what is the primary criterion used to group data points into subsets?
In clustering, what is the primary criterion used to group data points into subsets?
How is similarity typically assessed for numeric variables in clustering algorithms?
How is similarity typically assessed for numeric variables in clustering algorithms?
How is similarity assessed for categorical variables?
How is similarity assessed for categorical variables?
What do features (columns) represent in a dataset analyzed using clustering techniques?
What do features (columns) represent in a dataset analyzed using clustering techniques?
How do distance-based algorithms behave in the presence of outliers?
How do distance-based algorithms behave in the presence of outliers?
What is the primary purpose of feature scaling in clustering?
What is the primary purpose of feature scaling in clustering?
Which of the following are common methods of cluster analysis?
Which of the following are common methods of cluster analysis?
Which of the following is a characteristic of hierarchical clustering?
Which of the following is a characteristic of hierarchical clustering?
For what type of dataset size is the application of hierarchical clustering most appropriate?
For what type of dataset size is the application of hierarchical clustering most appropriate?
What kind of data is K-means clustering used for?
What kind of data is K-means clustering used for?
What is the role of Euclidean distance in K-means clustering?
What is the role of Euclidean distance in K-means clustering?
What does the output of K-means produce?
What does the output of K-means produce?
Which principle about distance is commonly true in K-means clustering?
Which principle about distance is commonly true in K-means clustering?
In the context of unsupervised learning, what is the role of summarizing the properties of each cluster?
In the context of unsupervised learning, what is the role of summarizing the properties of each cluster?
How can unsupervised learning be used as a prelude to classification?
How can unsupervised learning be used as a prelude to classification?
Which of the following scenarios exemplifies a use case for unsupervised learning?
Which of the following scenarios exemplifies a use case for unsupervised learning?
How does the absence of labeled examples in unsupervised learning affect the learning process for an algorithm?
How does the absence of labeled examples in unsupervised learning affect the learning process for an algorithm?
What does it mean when we say 'There is no one correct answer' in clustering?
What does it mean when we say 'There is no one correct answer' in clustering?
If a dataset contains outliers, what is the most suitable first step for distance-based algorithms?
If a dataset contains outliers, what is the most suitable first step for distance-based algorithms?
What is the most appropriate number of clusters to start with in hierarchical clustering?
What is the most appropriate number of clusters to start with in hierarchical clustering?
What is the result of a dataset that results to 0 in Euclidean distance?
What is the result of a dataset that results to 0 in Euclidean distance?
Why is unsupervised learning considered an exploratory technique?
Why is unsupervised learning considered an exploratory technique?
What type of diagram represents a dendrogram?
What type of diagram represents a dendrogram?
Which of the following steps must occur first?
Which of the following steps must occur first?
What is the difference when looking at stores versus customers in terms of dataset size?
What is the difference when looking at stores versus customers in terms of dataset size?
What makes a feature have a dominant influence over the model?
What makes a feature have a dominant influence over the model?
Why is the goal to identify homogeneous subsets?
Why is the goal to identify homogeneous subsets?
What is the use of exploratory data analysis?
What is the use of exploratory data analysis?
What does density mean in the context of unsupervised learning?
What does density mean in the context of unsupervised learning?
If you started K-means with 10 clusters, and the second run through creates 10 different clusters. What can you do?
If you started K-means with 10 clusters, and the second run through creates 10 different clusters. What can you do?
Flashcards
Unsupervised Learning
Unsupervised Learning
A type of machine learning where the algorithm learns patterns from unlabeled data.
Clustering
Clustering
Assigning data points to subgroups based on inherent similarities.
Segmentation Technique
Segmentation Technique
Another term for clustering; dividing data into distinct segments.
Similarity
Similarity
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Boxplot
Boxplot
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Feature Scaling
Feature Scaling
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Hierarchical Clustering
Hierarchical Clustering
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Dendrogram
Dendrogram
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K-Means Clustering
K-Means Clustering
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Centroid
Centroid
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Study Notes
- Lecture 10 covers unsupervised learning
Unsupervised Learning
- The learning algorithm presents a structure that is reviewed by a human
- It is an iterative process for finding relationships and meaningful patterns
- Unsupervised learning needs human supervision to select the learning algorithm, distance metrics, and feature selection
- It can be used as part of exploratory data analysis (EDA)
Measuring Unsupervised Learning
- There are no specific metrics to measure it
- Questions to consider include if an informative visualization of the data exists or if subgroups among observations or variables can be discovered
Clustering
- Also known as a segmentation technique, or division into separate parts
- There is no one correct answer, the approach depends on the goals and constraints from available data
- The approach is based on similarity to identify homogeneous subsets
Similarity
- For numeric variables, it is based on distance (delta between values)
- For categorical variables, it is based on having the same values
- Each column in a dataset represents a dimension with the possibility of similarity between rows
- Measuring distance between the features reveals the concentration to form clusters
Boxplot
- Distance-based algorithms are sensitive to outliers -Points beyond the "whiskers” are considered outliers
Feature Scaling
- Feature scaling transforms the values of a feature to a common scale such as 0 to 1, is the way of dealing with outliers
- It is applied to allow all features to contribute to the model, without larger-scale features dominating it
Cluster Analysis
- Hierarchical and K-Means are types of analyses
Hierarchical Clustering
- It is an iterative and computationally intensive process
- Begin with one cluster, split and continue splitting until complete
- Difficult to do on large datasets
- Best used when meaningful to a small dataset -For example, when looking at stores vs customers
K Means Clustering
- Used for clustering numerical data, usually a set of measurements about objects of interest
- Requires numerical input and a defined distance metric, such as Euclidean distance, over the variable space
- Output consists of the centers of each discovered cluster and the assignment of each input dataset to a cluster
- Centroid
K Means Clustering Distances
- Two distance measures are defined in K-means: the distance between two data points (records) and the distance between two clusters
- Distance can be calculated in a number of ways, but four principles tend to hold true
-Distance is not negative
-Distance from one record to itself is zero - The distance from record I to record J is the same as from record J to record I
- The distance between two records can not be greater than the sum of the distances between each record and a third record
Unsupervised Learning Use Cases
- Often used as an exploratory technique to discover structure in the data and summarize the properties of each cluster
- Sometimes used as a prelude to classification -To discover classes -Household income -Yearly purchase amount in dollars -Number of household members of customer households
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