Podcast
Questions and Answers
How does unsupervised learning differ from classical programming in terms of input and output?
How does unsupervised learning differ from classical programming in terms of input and output?
- Classical programming and unsupervised learning both take rules and data as input and provide answers as output.
- Classical programming takes data as input and provides rules as output, while unsupervised learning takes rules and data as input and provides answers as output.
- Classical programming takes rules and data as input and provides answers as output, while unsupervised learning takes only data as input and provides segmented data as output. (correct)
- Classical programming takes answers as input and provides data as output, while unsupervised learning takes rules as input and provides data as output.
Which of the following is a key characteristic of unsupervised learning?
Which of the following is a key characteristic of unsupervised learning?
- Minimizing the need for iterative processes in finding patterns.
- The learning algorithm presents a structure for human review. (correct)
- Providing labeled examples for the algorithm to learn from.
- A single, definitive solution is always guaranteed.
How is the success of unsupervised learning typically evaluated?
How is the success of unsupervised learning typically evaluated?
- By determining the R-squared value of the model.
- By assessing if the data can be visualized informatively and if subgroups can be discovered. (correct)
- By using predefined metrics to measure accuracy.
- By calculating the precision and recall of the clustered data.
Which aspect of unsupervised learning necessitates 'human supervision'?
Which aspect of unsupervised learning necessitates 'human supervision'?
What role can unsupervised learning play in exploratory data analysis (EDA)?
What role can unsupervised learning play in exploratory data analysis (EDA)?
If clustering is also known as a 'segmentation technique,' what does this imply about the process?
If clustering is also known as a 'segmentation technique,' what does this imply about the process?
What is the primary goal when identifying homogeneous subsets in clustering?
What is the primary goal when identifying homogeneous subsets in clustering?
In the context of clustering, how is 'similarity' typically determined for numeric variables?
In the context of clustering, how is 'similarity' typically determined for numeric variables?
Given a dataset, what does each feature (column) represent in the context of clustering?
Given a dataset, what does each feature (column) represent in the context of clustering?
How do distance-based algorithms react to outliers in a dataset?
How do distance-based algorithms react to outliers in a dataset?
Why is feature scaling used to handle outliers in clustering?
Why is feature scaling used to handle outliers in clustering?
Which of the following are common methods of cluster analysis?
Which of the following are common methods of cluster analysis?
What is a key characteristic of hierarchical clustering?
What is a key characteristic of hierarchical clustering?
Under what circumstances is hierarchical clustering most appropriate?
Under what circumstances is hierarchical clustering most appropriate?
What type of data is K-Means clustering generally used for?
What type of data is K-Means clustering generally used for?
What specific type of input is required for K-Means clustering?
What specific type of input is required for K-Means clustering?
What are the key outputs of the K-Means clustering algorithm?
What are the key outputs of the K-Means clustering algorithm?
Within K-means clustering, what must be true of the distance between two records?
Within K-means clustering, what must be true of the distance between two records?
How does distance relate with the data points (records) in K-means clustering?
How does distance relate with the data points (records) in K-means clustering?
In the context of unsupervised learning, what is meant when it is described as an 'exploratory technique'?
In the context of unsupervised learning, what is meant when it is described as an 'exploratory technique'?
What is the role of unsupervised learning with classification?
What is the role of unsupervised learning with classification?
Which of the following is an appropriate use case for unsupervised learning?
Which of the following is an appropriate use case for unsupervised learning?
What type of variables can be used to see the use case?
What type of variables can be used to see the use case?
In clustering, under what conditions would you describe a subset as being 'homogeneous'?
In clustering, under what conditions would you describe a subset as being 'homogeneous'?
How does unsupervised learning enable visualization?
How does unsupervised learning enable visualization?
When using unsupervised learning to visualize data, what are two specific questions that could be asked?
When using unsupervised learning to visualize data, what are two specific questions that could be asked?
Besides Hierarchical Clustering, which following methods fall along the same analysis?
Besides Hierarchical Clustering, which following methods fall along the same analysis?
In the process of K-means, what should the sum of the distances be between records
In the process of K-means, what should the sum of the distances be between records
What is a use case that can have unsupervised learning within 7-Eleven?
What is a use case that can have unsupervised learning within 7-Eleven?
In the context of categorical variables, how is 'similarity' typically determined?
In the context of categorical variables, how is 'similarity' typically determined?
Which clustering helps determine similarity density?
Which clustering helps determine similarity density?
Which statement describes feature scaling as a method to handling outliers?
Which statement describes feature scaling as a method to handling outliers?
How does determining distance compare with Distance from record I to J in K-Means?
How does determining distance compare with Distance from record I to J in K-Means?
What should be the principle held true about negative values.
What should be the principle held true about negative values.
Why should the algorithm be selected manually?
Why should the algorithm be selected manually?
How does measuring with K-means distance from one record relate to itself?
How does measuring with K-means distance from one record relate to itself?
What should be consider the amount of clusters when using Hierarchical Clustering.
What should be consider the amount of clusters when using Hierarchical Clustering.
Flashcards
Unsupervised Learning
Unsupervised Learning
A type of machine learning where the algorithm learns from unlabeled data to identify patterns and structures without explicit guidance.
Unsupervised Learning Process
Unsupervised Learning Process
Providing data to an algorithm, which then presents a structure for human review, allowing iterative discovery of patterns and relationships.
Measuring Unsupervised Learning
Measuring Unsupervised Learning
There are no direct metrics; instead, it involves visualizing data informatively and discovering subgroups.
Human Supervision in Unsupervised Learning
Human Supervision in Unsupervised Learning
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Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA)
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Clustering
Clustering
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Clustering Approach
Clustering Approach
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Similarity Measurement
Similarity Measurement
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Similarity in Datasets
Similarity in Datasets
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Outlier Sensitivity
Outlier Sensitivity
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Feature Scaling
Feature Scaling
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Cluster Analysis Types
Cluster Analysis Types
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Hierarchical Clustering
Hierarchical Clustering
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K-Means Clustering
K-Means Clustering
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Distance Measurement Principles
Distance Measurement Principles
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Unsupervised Learning Uses
Unsupervised Learning Uses
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Study Notes
- Machine Learning 1 is covered in Lecture 10, focusing on Unsupervised Learning.
- Unsupervised Learning can be used to organize blocks or books.
Unsupervised Learning
- There is data that goes into machine learning, which produces segmented data.
- No "labeled examples" of the correct answer are provided.
- A learning algorithm presents a structure for human review.
- It's an iterative process that allows for finding meaningful patterns and relationships.
- Unsupervised Learning asks the questions related to data visualization and discovery of subgroups.
- Requires human supervision and the algorithm searches.
- Learning algorithm and distance metrics are selected.
- It's used as part of exploratory data analysis (EDA).
Clustering
- Clustering is a type of Unsupervised Learning.
- Another name for it is a segmentation technique, involving dividing info into separate parts.
- There is no one correct answer for the approach, it depends on the goals and is constrained by available data.
- Homogeneous subsets are identified based on similarity within the subset and the number of subsets.
Similarity
- Approach is based on similarity.
- For numeric variables, similarity uses distance (delta between values).
- For categorical variables, similarity is based on having the same values.
- Each feature (column) in a dataset is a dimension with potential similarity between instances (rows).
- Distance between features is measured to find "density," i.e., points of concentration forming clusters.
- Distance-based algorithms can be impacted by the presence of outliers.
- Points beyond the "whiskers" are considered outliers
Feature Scaling
- Outliers are addressed via feature scaling by transforming the values of a feature to a common scale, such as between 0 and 1.
- Feature scaling is applied so all features have an equal opportunity to contribute, preventing larger-scale features from dominating the model.
Cluster Analysis
- Cluster Analysis' include:
- Hierarchical
- K-Means
Hierarchical Clustering
- It's an iterative process of starting with one cluster and splitting until done.
- It's computationally intensive and difficult to do on large datasets.
- Use it when it's meaningful to a small dataset, like stores vs customers.
K-Means Clustering
- It's used for clustering numerical data, like a set of measurements about objects of interest.
- The input must be numerical, with a defined distance metric over the variable space like Euclidian distance.
- The Output is the centers of each discovered cluster, and the assignment of each input datum to a cluster (Centroid).
- In K-means two measures of distances are defined - the distance between two data points and the distance between two clusters.
- Distance may be calculated in a number of ways, but 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
- Exploratory technique to discover data structures and summarize cluster properties.
- Prelude to classification for "discovering the classes."
- Examples of use cases include - household income, yearly purchase amount in dollars, the number of household members in customer households.
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