Podcast
Questions and Answers
When the count of distinct values in a dataset approaches the number of records, what is the likely consequence?
When the count of distinct values in a dataset approaches the number of records, what is the likely consequence?
- The computational complexity of processing the data is significantly reduced.
- Information derived from the data is enhanced, leading to more accurate models.
- Data sparcity decreases, improving the reliability of statistical analysis.
- Meaning derived from the data diminishes due to increased uniqueness. (correct)
Which of the following strategies is most suitable for resolving issues caused by high uniqueness in a dataset?
Which of the following strategies is most suitable for resolving issues caused by high uniqueness in a dataset?
- Applying one-hot encoding to categorical variables.
- Using imputation techniques to fill missing values.
- Increasing the precision of numerical values.
- Discretization followed by binarization of continuous variables. (correct)
How does an increase in dimensionality typically affect the sparcity of a dataset?
How does an increase in dimensionality typically affect the sparcity of a dataset?
- Sparsity increases because the available data is spread thinly across a larger feature space. (correct)
- Sparsity decreases because the data points become more densely packed.
- Sparsity initially increases but then decreases after a certain dimensionality threshold is reached.
- Dimensionality has no direct impact on data sparcity.
What is the primary goal of Principal Component Analysis (PCA)?
What is the primary goal of Principal Component Analysis (PCA)?
In the context of machine learning, what is one potential drawback of high dimensionality?
In the context of machine learning, what is one potential drawback of high dimensionality?
Flashcards
Uniqueness in Data
Uniqueness in Data
When the number of unique values in a feature approaches the total number of records, the feature's ability to provide meaningful information decreases.
Discretization
Discretization
A process of transforming continuous variables into discrete or categorical variables, often by grouping values into bins or categories.
Dimensionality & Sparsity
Dimensionality & Sparsity
When data becomes sparse as the number of dimensions (features) increases. This can lead to issues with model performance.
Principal Component Analysis (PCA)
Principal Component Analysis (PCA)
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Correlation
Correlation
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Study Notes
- Lecture II focuses on Dimensionality for Machine Learning in Business with Michael Deamer at Fordham Gabelli School of Business
Uniqueness
- Meaning decreases as the count of distinct values nears the number of records
- Information decreases if the number gets close to 0
Resolving Uniqueness
- Includes examples of data transformation such as discretization to binarization
Correlation
- Demonstrates visual representations of correlation matrices for different features of the data
Dimensionality
- As the number of dimensions increases, so does the sparcity of the data
Principle Component Analysis
- Demonstrates a method for reducing the dimensionality of the data, creating new variable components
- Includes a 3D visual reference
To Do:
- Includes to dos such as Lab 1, Homework 1 and recommended reading of 6.2
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Description
Lecture on dimensionality for machine learning in business. The lecture includes discussion of uniqueness and correlation, and demonstrates methods for dimensionality reduction. Principle Component Analysis is visually demonstrated.
