Learning Path
Question & Answer
Choose the Best Answer
By reducing the dimensionality and retaining variance
By increasing the number of variables in the analysis
By eliminating outliers from the dataset
By creating more complex models
Understanding the Answer
Let's break down why this is correct
PCA looks for directions that hold the most variation in the data. Other options are incorrect because Some think adding more variables helps, but PCA actually removes variables; PCA does not automatically delete outliers.
Key Concepts
Matrix Decomposition Techniques
medium level question
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Deep Dive: Matrix Decomposition Techniques
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Definition
Matrix decomposition techniques, such as singular value decomposition and eigen decomposition, are essential methods in linear algebra that allow for the simplification and understanding of complex functions. These techniques enable students to break down matrices into simpler components, facilitating easier analysis and programming adjustments, akin to refactoring code in software development. Understanding these methods is significant in Business applications, particularly in data analysis and machine learning, where efficient data processing is crucial.
Topic Definition
Matrix decomposition techniques, such as singular value decomposition and eigen decomposition, are essential methods in linear algebra that allow for the simplification and understanding of complex functions. These techniques enable students to break down matrices into simpler components, facilitating easier analysis and programming adjustments, akin to refactoring code in software development. Understanding these methods is significant in Business applications, particularly in data analysis and machine learning, where efficient data processing is crucial.
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