Other Topics — Machine Learning Interview Questions
Introduction
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In the ruthless and cut-throat competitive world of today, the stakes to crack an interview soar high in the sky. Most of the candidates find it challenging to get through the recruitment process. Every interview is a new learning experience, even though you’ve appeared in many interviews. It can be a challenging situation because you will have to answer the baffling questions reasonably and satisfactorily.
In this article, I will present some interview questions and answers related to Principal Component Analysis. The goal is to guide you on successfully answering these questions because the interviewers will be testing you on these topics, which can be a very stressful situation. So, let’s start briefly by first understanding what PCA is and how it works.
Article Overview
- What is Principal Component Analysis?
- How does Principal Component Analysis work?
- PCA ML Interview Questions & Answers
- Conclusion
What is Principal Component Analysis?
You know that the model is likely to underfit when enough features are not present in the data. Similarly, it is expected to overfit or underfit when data contains too many features. This phenomenon is known as the curse of dimensionality.
Principal Component Analysis(PCA) is a dimensionality reduction technique that reduces the dimensionality of large data sets to improve classification accuracy. It transforms a large number of variables into smaller and optimal numbers of lower dimensionality features while preserving as much information as possible. So, our primary target is to reduce the dimensions of our dataset to trade a little accuracy. This is because we can explore and visualize smaller datasets more easily than larger ones and make the analysis faster for machine learning algorithms without processing extraneous variables.
How Principal Component Analysis Works?
We can use PCA to transform a large number of variables into a smaller number of independent variables by performing the following steps:
- Standardizing PCA
- Calculating the covariance matrix
- Determining the eigenvalues and eigenvectors for the covariance matrix
- Vector Plotting on the scaled data
PCA ML Interview Questions/Answers
Do you know what type of questions are asked in ML interviews related to PCA? If not, then don’t worry. You are at the right place where I will guide you through the table that contains certain PCA questions likely to be asked in Data Science and Machine Learning interviews. Answers to the questions are also provided to give you a clear understanding of concepts.
What do you know about the curse of dimensionality?
A: Problems arise when we work with data in higher dimensions. The number of samples increases due to the rise in the number of features, thus resulting in a complex model. This phenomenon is termed the curse of dimensionality. There are chances that our model undergoes overfitting due to a large number of features. So, it performs poorly on test data as it becomes heavily dependent on training data.
What is Principal Component Analysis?
A: PCA is a well-known dimensionality reduction algorithm that transforms a large set of correlated variables into smaller numbers of unrelated variables, called the principal components. The purpose is to exclude additional features while maintaining most of the variability in the dataset.
What are the advantages of PCA?
PCA has the following advantages:
- Removes noise and redundant features
- Less storage space and computational resources are used as data get less.
- Model accuracy is improved as there is less misleading data
- Machine learning algorithms train faster, and we can also visualize the data on 2D or 3D plots.
How do you select the first principal component axis?
We select the first principal component in a way that it contains most of the data points, thus having the highest variance.
What are the drawbacks of dimensionality reduction?
Dimensionality reduction has some of the disadvantages as:
- The reduction can be computationally intensive.
- It can be hard to interpret the transformed independent variables.
- As we reduce the number of features, some information is lost, due to which the performance of the algorithms degrades.
Why do we do standardization before using PCA?
We do standardization because we have to assign equal weights to all the variables; otherwise, we can get misleading directions. So, we need to standardize if all the variables are not on the same scale.
Name some of the techniques that are used for dimensionality reduction.
The techniques to reduce the dimensionality of a dataset are:
- Feature Selection – We select or eliminate attributes as we test them on the basis of their worth.
- Feature Extraction – We create a reduced set of features from the existing ones and summarize most of the information in our dataset.
Is PCA a feature selection technique?
No, PCA is not a feature selection technique. It is a feature extraction technique that decreases the dimensionality of the dataset and captures as much variance in the data as possible. It sorts the dimensions on the basis of their contribution to the predictability of the model, thus discarding the low significant features.
Name some of the areas where PCA has its applications?
PCA has applications in areas like:
- Data Visualization
- Data Compression
- Noise Reduction
- Image Compression
- Face Recognition
What happens when the eigenvalues become approximately equal?
PCA can not select the principal components if all eigenvalues are roughly equal. This is because all principal components become equal.
What happens when we don’t rotate the PCA components?
The effect of PCA will diminish if we don’t rotate the components. Then, we will have to select more components to explain variance in the training data.
Conclusion
We know that large datasets are increasingly common, and it is often difficult to interpret them. Principal Component Analysis reduces the dimensionality of such datasets, increases interpretability but also minimizes information loss. It successively maximizes variance by creating new uncorrelated variables. As far as the interview questions are concerned, you need to practice them a lot by keeping in mind some of the following tips:
- Practice coding questions, and you must be able to communicate your thought process.
- You must have problem-solving abilities and flexibility to adjust in the real industry environment as things pop up that never actually go as expected.
- You need to focus on the theory and learn how to implement it. Also, you should have three to four stories regarding your personal projects that you can tell to the interviewers.
