What’s the ultimate mission of PCA (Principal Component Analysis)?
A) Shrink the dimensions like a magic spell! ?
B) Grow the features like a garden! ?
C) Sweep away the noise like a broom! ?
D) Get everything on the same page like a rulebook! ?
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Answer: A) To reduce the dimensionality of the dataset
Explanation: PCA reduces the number of features in a dataset by transforming the original features into a smaller set of new features (principal components) that retain most of the original variance.
Is it true or false?
PCA can join the party for both supervised and unsupervised learning tasks!
A) True ?
B) False ?
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Answer: A) True
Explanation: While PCA is primarily an unsupervised technique for dimensionality reduction, it can be used as a preprocessing step in supervised learning to reduce feature dimensionality.
What’s the secret power of the covariance matrix in PCA?
A) To give the data a makeover (normalize it) ?
B) To uncover the main characters (principal components) ?????
C) To group the data into squads (cluster it) ?
D) To resize everything (scale the data) ?
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Answer: B) To identify the principal components
Explanation: The covariance matrix captures the relationships between features. Its eigenvalues and eigenvectors are used to determine the principal components.
In the PCA universe, what role do eigenvalues play?
A) They’re the secret codes (coefficients) of the principal components ?
B) They show off the size of the variance (magnitude) in the data ?
C) They’re the shadows (projections) of the original data on the new axes ?
D) They’re the angles between the new stars (principal components) ?
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Answer: B) They represent the magnitude of the variance in the data
Explanation: Eigenvalues indicate the amount of variance captured by each principal component. Higher eigenvalues correspond to components with more significant variance.
What’s the best description of an eigenvector’s superpower in PCA?
A) A vector that points to where the magic happens (maximum variance) ?
B) A vector that stretches or shrinks the data points ?
C) A vector that gathers data into groups (clusters) ?
D) A vector that gives the data a fresh look (normalizes) ?
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Answer: A) A vector that determines the direction of maximum variance
Explanation: Eigenvectors define the directions of the new feature space (principal components) that capture the maximum variance in the data.
Why should you give your data a makeover (standardize it) before diving into PCA?
A) So every feature gets a fair shot (contributes equally) in the analysis ??
B) To make your data look extra fancy (more visually appealing) ?
C) To magically fill in any blanks (eliminate missing values) ?
D) To bulk up your dataset (increase the number of features) ?
Show answer
Answer: A) To ensure all features contribute equally to the analysis
Explanation: Standardizing the data ensures that features with larger scales do not dominate the variance, allowing PCA to consider all features equally.
True or False: Can PCA play detective and spot the outliers in your data?
A) True ?????
B) False ????
Show answer
Answer: A) True
Explanation: PCA can help detect outliers by identifying points that deviate significantly from the principal components, which represent the main structure of the data.
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