How do you explain PCA in an interview?

Interview Questions on PCA

1. Why do we need dimensionality reduction?
2. Explain Principal Component Analysis, assumptions, equations.
3. Can PCA be used to reduce the dimensionality of a highly nonlinear dataset?
4. Limitations of PCA?
5. Is rotation necessary in PCA?
6. Is it important to standardize before applying PCA?

How do you choose principal components?

A widely applied approach is to decide on the number of principal components by examining a scree plot. By eyeballing the scree plot, and looking for a point at which the proportion of variance explained by each subsequent principal component drops off. This is often referred to as an elbow in the scree plot.

What is factor rotation in PCA?

Varimax rotation is an important second step in Factor Analysis and Principal Component Analysis. The initial factor analysis step has an infinite number of initial, or provisional, factors. Factor rotation, including Varimax rotation, transforms the initial factors into new ones that are easier to interpret.

How do you choose principal components in PCA?

Are all principal components orthogonal?

The principal components are the eigenvectors of a covariance matrix, and hence they are orthogonal. Importantly, the dataset on which PCA technique is to be used must be scaled. The results are also sensitive to the relative scaling. As a layman, it is a method of summarizing data.

What is principal component analysis (PCA)?

Find out who’s hiring in Chicago. What Is Principal Component Analysis? Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.

What is the use of PCA in machine learning?

Practically PCA is used for two reasons: Dimensionality Reduction: The information distributed across a large number of columns is transformed into principal components (PC) such that the first few PCs can explain a sizeable chunk of the total information (variance). These PCs can be used as explanatory variables in Machine Learning models.

What is the difference between PCA and 10-dimensional data?

So, the idea is 10-dimensional data gives you 10 principal components, but PCA tries to put maximum possible information in the first component, then maximum remaining information in the second and so on, until having something like shown in the scree plot below.

What does the PCA score plot tell us?

The PCA score plot of the first two PCs of a data set about food consumption profiles. This provides a map of how the countries relate to each other. The first component explains 32\% of the variation, and the second component 19\%. Colored by geographic location (latitude) of the respective capital city. How to Interpret the Score Plot