PCA Questions and Answers
Question: PCA stands for Principal _________ Analysis.
Component
Difficulty: Easy
Explanation: PCA stands for Principal Component Analysis. It's a dimension reduction technique that enables you to identify the most significant underlying structure of your data.
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PCA Questions and Answers
Question: PCA stands for Principal _________ Analysis. - answer Component
Difficulty: Easy
Explanation: PCA stands for Principal Component Analysis. It's a dimension reduction
technique that enables you to identify the most significant underlying structure of your
data.
Question: PCA aims to find a new set of dimensions such that all the dimensions are
_________ and the variance is maximized. - answer Orthogonal
Difficulty: Medium
Explanation: In PCA, the goal is to find a set of new dimensions that maximize variance
and are orthogonal to (independent of) each other. This is achieved by projecting the
data onto new axes, which are called principal components.
Question: The first principal component explains the _________ amount of variance in
the data. - answer Largest
Difficulty: Medium
Explanation: The first principal component is the direction in the data that explains the
largest amount of variance. Each subsequent component explains the maximum
variance possible under the constraint that it is orthogonal to the preceding
components.
Question: PCA assumes that the data variables have a _________ distribution. -
answer Gaussian or Normal
Difficulty: Medium
Explanation: PCA is a method that relies on linear algebra and is generally more suited
for data that has a linear structure. It assumes that the data variables have a Gaussian
(or Normal) distribution.
Question: The matrix that contains all the principal components is called the _________
matrix. - answer Eigenvector
Difficulty: Hard
Explanation: An eigenvector is a direction. In the context of PCA, it is a direction in the
space of input features. The collection of all eigenvectors creates a matrix that we can
use to transform our data.
Question: PCA is often used before applying a machine learning algorithm to
_________ the dimensionality of the input data. - answer Reduce
Difficulty: Medium
Explanation: PCA is a method that reduces the dimensionality of the data while
retaining most of the important information. It is often used as a pre-processing step
before applying a machine learning algorithm, as it can reduce computational cost and
alleviate the problem of overfitting.
, Question: The _________ of a matrix are used in PCA to form the new feature
dimensions. - answer Eigenvectors
Difficulty: Medium
Explanation: PCA involves computing the eigenvectors of the covariance matrix of the
data. These eigenvectors form the new axes or dimensions of the data, which are used
to project the original data points into the new feature space.
Question: In PCA, the amount of variance that each PC explains is quantified by the
corresponding _________. - answer Eigenvalue
Difficulty: Hard
Explanation: In PCA, each principal component (PC) has a corresponding eigenvalue,
which is a measure of the amount of variance in the data that is explained by that PC.
Higher eigenvalues correspond to higher amounts of explained variance.
Question: The principal components of PCA are selected based on their corresponding
_________ values. - answer Eigenvalue
Difficulty: Medium
Explanation: The eigenvalues corresponding to each of the principal components
determine the amount of variance captured by each component. The components are
sorted according to the eigenvalues, in decreasing order, and the top few components
(those with the largest eigenvalues) are selected.
Question: PCA tends to perform poorly when the data variables have _________
relationships. - answer Non-linear
Difficulty: Hard
Explanation: PCA is a linear method and assumes linear relationships among variables.
It tends to perform poorly when the relationships among variables are non-linear. For
data with non-linear relationships, other methods like Kernel PCA or non-linear
dimension reduction methods might be more suitable.
Question: One of the main uses of PCA in machine learning is for _________ reduction,
which helps to alleviate the curse of dimensionality. - answer Feature
Difficulty: Medium
Explanation: PCA is commonly used for feature reduction in machine learning. By
transforming the data to a lower-dimensional space, PCA can help alleviate the curse of
dimensionality, which is the problem that arises when dealing with high-dimensional
data, such as increased computational complexity and overfitting.
Question: PCA is a form of _________ learning, since it learns the components based
solely on the input data, without using any class label information. - answer
Unsupervised
Difficulty: Medium
Explanation: PCA is an unsupervised method because it doesn't use any class label
information. Instead, it transforms the input data based solely on the variance and
structure within the data itself.
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