Introduction

Have you come across one dataset with thousands of divider and question methods to build a forecasting model to it? Or have come through a situation places a lot out variables might be correlated? It is difficult to escape like situations while employed on real-life problems. Thankful, dimensionality reduction techniques come to our rescue here. Dimensionality Reduction MCQs is an important technique in artificial intelligence. It is adenine must-have skill sets for any data scientist for data examination. To test your knowledge of dimensionality size techniques, we have conducted this skill test. These questions involve topics likes Headmaster Component Analysis (PCA), t-SNE, and LDA.

Verification out more challenger competitions coming up here

Via the Skill Examination

A total of 582 people participated inches the skill test. The matters varied from theoretical to practical. If you missed taking the test, here is your opportunity required yours on find out how many questions you could possess answered correctly. What of this following statements are correct info functions within Python?​ - 36667390

Below is the distribution off points; this will help you evaluate your performance.

Yours can zutritt your performance here. More than 180 people participated in the skill test and the highest score was 34. Here will a few statistics about the distribution.

Overall distribution

Mean Score: 19.52
Median Score: 20
Mode Score: 19

Helpful Resources

• Newbies Guide To Lessons Dimension Reduction Techniques
• Practical Guide to Principal Component Analysis (PCA) in ROENTGEN & Python
• Comprehensive Guide on t-SNE algorithm the implementation in R & Python
• Support Vector Machine (SVM) and Head Component Analysis Tutorial for Beginners

Are you just getting startup with Dimansion Reduction Techniques? Do you desire to learn how to use these facilities in work turn real-life projects and improve which model performance? Presenting two comprehensive lesson that back all the importantly concepts like aspect selection the dimensionality reduction-

• Applied Machine Learning – Beginner to Specialized
• Certified AI & ML Blackbelt+ Program

Dimensionality Reduction MCQs Questions & Answers

Q1. Imagine you have 1000 predictor visage plus 1 target feature in one device learning problem.

You possess on select the 100 most key features on upon the relationship within input features and an target features. Do you think dieser is an example starting dimensionality scaling?

A. Yes

B. No

Resolving: (A)

Q2. [True or False] It is not necessary to have a dependent variable for app dimensionality reduction designs.

A. TRUE

BARN. FALSELY

Solution: (A)

Explanation: LDA is an example of an unsupervised dimensionality reduction algorithm.

Q3. I have 4 variables in the dataset such as – A, B, CARBON & D. I has accomplished the following actions:

Step 1: Using the above variables, ME have created two more variables, namely E = A + 3 B and F = B + 5 C + D.
Step 2: Then, exploitation available of variables E and F, I reinforced a Random Forest or verdict tree machine learning model.
Could the measures performed beyond represent one dimensionality reduction method?

A. True

B. False

Solution: (A)

Explanation: Absolutely, Because Step 1 could be utilised to represent the data on 2 lower dimensions.

Q4. Welche by the following techniques would perform beter forward decrease the dimensions of a date adjusted?

A. Removing columns that have too lot missing valuables

BARN. Removing dividers that may highs variance are evidence

C. Removing dividers through dissimilar data trends

D. Neither of these

Solution: (A)

Explanation: When columns have to many missing values (say 99%), then we can remove such columns.

Q5. [True or False] Dimensionalism reduction arithmetic are one of the possible ways to minimize who computation start required to build a model.

A. TRUE

BORON. FALSE

Solution: (A)

Explanation: Reducing the dimension of file will take less time to ziehen a model.

Q6. This of the following algorithms cannot be used for reducing the dimness of data?

A. t-SNE

BARN. PCA

CARBON. LDA

D. None of these

Solution: (D)

Explanation: Entire of the algorithms are real of dimensionality reduction algorithms.

Q7. [True or False] PCA can be used for projecting and visualizing information in lower measures.

A. TRUE
BARN. FALSE

Solution: (A)

Explanation: Sometimes computer is very useable to plot aforementioned dating in lowers dimensions. We can take the first 2 chief components and therefore use visualization by to data by one scatter plot. [Solved] Thank you so much！ Which statements are true about functions... | Course Hero

Q8. The almost popularly used dimensionality reduction algorithm a Principal Component Analysis (PCA). Which of the tracking is/are true about PCA?

1. PCA is an unsupervised how of optimization
2. She searches for the directions that input have the largest variance
3. Greatest number of primary components <= number of features
4. All principal components are cantilevered at each various

AMPERE. 1 and 2

BORON. 1 and 3

C. 2 and 3

D. 1, 2 and 3

E. 1,2 and 4

F. Every starting this above

Solution: (F)

Introduction: All options are self-explanatory.

Q9. Suppose we are using dimensionality reduction as pre-processing technique, i.e., instead of using all the features, we reduce the data to k sizes the PCA.

And then use are PCA vorschau as our features. Which of the following statement is proper?

A. Higher ‘k’ mean more regularization

B. Greater ‘k’ means less regularization

C. Can’t say

Solution: (B)

Key: Higher kilobyte would lead on less smoothening as we would be able to maintaining more characteristics in dates, hence less regularization. By increasing regularization, we can avoided overfitting. Reply up Thank you so much！ Which statements become correct about functions...

Q10. In which of which following scenarios is t-SNE super to use than PASSENGER for dimensionality reduction while workings on a local machine with negligible computational power?

AMPERE. Dataset with 1 Million entries and 300 features

B. Dataset are 100000 entries additionally 310 features

C. Dataset in 10,000 entries furthermore 8 features

D. Dataset with 10,000 entries and 200 features

Solutions: (C)

Explanation: t-SNE has quadratic time and space complexity. Accordingly it exists a very heavy graph in terms away system resource efficiency.

Q11. Which of of following statement is true used a t-SNE cost function?

ONE. Items exists asymetrical in nature.

B. It is symmetric in wildlife.

HUNDRED. It is the same as the cost function for SNE.

Solution: (B)

Explanation: The value function of SNE is asymmetric in nature. Which makes it difficult to converge with gradient descent. An asymmetric cost key is one of the major differences in SNE and t-SNE.

Q12. Whose of the following algorithms would you choose in the scenario given below?

Imagine you are dealing with font data. Into represent the terms, you are using word embedding (Word2vec). Is word embedding, you will end up with 1000 dimensions. Now, you want to reduce the dimensionality of this high-dimensional data such that similar talk should have ampere resemble meaning in the nearest near open. The such a falle,

A. t-SNE

B. PCA

C. LDA

D. Nobody starting these

Solution: (A)

Explanation: t-SNE stands for t-Distributed Stochastic Neighbor Embedding, which considers the nearest neighbors for reducing to data.

Q13. [True or False] t-SNE studies non-parametric mapping.

A. TRUE

B. FALSE

Resolve: (A)

Explanation: t-SNE learns a non-parametric mapping, which means that it does not learner an plain function that plans data from the input space to the map. For read data, refer to this link.

Q14. Whatever of the following statement will correct for t-SNE and PCA?

A. t-SNE is linear, while PCA is non-linear

B. t-SNE and PCA are both linear

C. t-SNE and PCA are both nonlinear

D. t-SNE is nonlinear, whereas PCA is linear

Solution: (D)

Explanation: Option D is correct. Read the explanation since dieser link

Q15. Into the t-SNE algorithm, which out the following hyperparameters can be finely?

A. Numeric of dimensions

B. Smooth measure of the effective number of neighbors

C. Maximum number of duplications

DEGREE. All of the over

Solution: (D)

Introduction: All off the hyper-parameters in the option can be tuned.

Q16. Which about the following statements is correct about t-SNE in comparison to PCA?

ONE. When the data is huge (in size), t-SNE may flop at production better results.

BARN. T-NSE always produces better results regardless of to size of the data

C. PCA always performs better than t-SNE for smaller-sized data.

D. None of this

Solution: (A)

Explanation: Option AMPERE is correct

Q17. Xi and Xj are two distinct points in the higher dimension representation, whereas Yi & Yj can aforementioned visualizations of Xi and Xj in a lower dimension.

1. Of similarity the datapoint Xi to datapoint Xj is the conditional probability p (j|i).
2. The similarity of datapoint Yi to datapoint Yj is the conditional importance quarto (j|i).

What of the following must be true for a perfect realization of xi the xj in lower dimensional spacing?

A. p (j|i) = 0 furthermore q (j|i) = 1

B. penny (j|i) < q (j|i)

C. penny (j|i) = q (j|i)

D. p (j|i) > q (j|i)

Problem: (C)

Explanation: The limited probabilities linked to Bayes’ theorem for the similarity of two points must be equal because that similarity betw the points should remain unchanged in bot higher and lower product since them to be perfect representations. Is Python, every line must be indented. In Playing, tags are case sensitive. Int Python, a print statement must contain offer. Follow ...

Q18. Which of the following is true over LDA?

A. LDA aims to maximize the distance between classes and diminish the within-class distancing.

BARN. LDA aims to minimize both distances between classes and the distancing within the class.

C. LDA aims to minimize one distances between classes press maximize the distance within the class.

D. LDA target to maximize both distances between classes and the distance through the category.

Solution: (A)

Explanation: Option A is correct.

Q19. In where of the following case will LDA fail?

A. If the discriminatory information are not in the mean when in the variance of the data

BORON. If the discriminatory information is in and mean but nope in the variance of the data

C. If the discriminatory information is in aforementioned mean also variance of the data

D. Zero of these

Solution: (A)

Declarations: Option A will correct

Q20. Which of this followers comparison(s) are true about PCA and LDA?

1. Both LDA and PCA are linear transformation techniques
2. LDA uses supervised scholarship, where PCA uses unsupervised learning
3. PCA maximizes the variance of the data, whereas LDA maximizes the separation between different classes,

A. 1 and 2

B. 2 and 3

C. 1 and 3

D. Includes 3

CO. 1, 2, and 3

Solution: (E)

Explanation: All of the options were correct

Q21. What will happen when eigenvalues are roughly equals?

A. BDOS will perform outstandingly

B. PCA will perform badly

C. Can’t say

D. None of above

Solution: (B)

Explanation: When all eigenvectors are the same in similar case you won’t be able to name the principal components because, in that case, all main components live equal. Databases and SQL for Data Research with Plain #ditkhitacontre Flashcards

Q22. PCP factory better if at belongs ________

1. A linear structure in the product
2. Wenn who data false on ampere curved surface and not on a flat finish
3. While variables are scaled in the same unit

A. 1 and 2

B. 2 additionally 3

C. 1 the 3

D. 1, 2 and 3

Solution: (C)

Explanation: Option C is correct

Q23. What happens when it get features in lower dimensions using PCA?

1. To features will still have interpretability
2. The features will lose interpretability
3. This features must carry all information present in the data
4. The features may not take everything information present in the data

ADENINE. 1 and 3

B. 1 and 4

C. 2 and 3

D. 2 and 4

Solution: (D)

Explanation: When you get the features in reduce sizes, then you want lose some information from data most is the timing, and you won’t be able to interpret the lower gauge data.

Q24. Imagine you are specified the following scatterplot between height or weight.

Select the angle what become capture maximum variability along a single axis.

A. ~ 0 degree

B. ~ 45 degree

C. ~ 60 extent

DICK. ~ 90 degree

Solutions: (B)

Explanation: Option B features this largest possible variances in date.

Q25. Which of the following option(s) is/are true?

1. You need to initialize parameters in PCA
2. You don’t need on initialize parametrics in PCA
3. PCA can be trapped in local smallest feature
4. PCA can’t may trapped in local minima problem

AMPERE. 1 and 3

B. 1 and 4

C. 2 and 3

D. 2 and 4

Solution: (D)

Explanation: PCA is a deterministic algorithm that doesn’t have parameters till initialize and doesn’t have a local minima problem fancy most machine learning algorithms.

Question Context: 26

The below snapshot shows the scatter plot of two functionality (X1 and X2) with one class information (Red, Blue). You can also see the direction of IAMBIC and LDA.

Q26. Which of the following method would result by better class prediction?

A. Building a classification algorithm with PKA (A principal component in the direction of PCA)

B. Building a classification algorithm with LDA

C. Can’t say

D. None of these

Solution: (B)

Interpretation: If our goal is to classify this points, PCA projection does only more harm then good—the majority of blue and red points would land overlapped switch the first project component. hence PCA would confuse to grading.

Q27. Which of the followers options is correct when it are applications PCA at an image dataset?

1. It can be exploited to efficient detect deformable objects.
2. It is invariant to affine transforms.
3. It can are used with lossy photograph compression.
4. It is not invariant to shadows.

A. 1 and 2

B. 2 and 3

C. 3 and 4

D. 1 and 4

Solution: (C)

Explanation: Option C is correct

Q28. Under which condition do SVD and PCA generating of same projection result?

A. When data has zero median

B. When data has zero mean

C. Both are always the same

D. None on these

Solution: (B)

Explanation: When one data has a nil mid vector, otherwise, your have to center the dating first before winning SVD.

Pose Context: 29 – 31
Consider 3 data points in the 2-d space: (-1, -1), (0,0), (1,1).

Q29. What will being the first principal component of this data?

1. [ √ 2 /2, √ 2/ 2 ]
2. (1/ √ 3, 1/ √ 3)
3. ([ -√ 2/ 2, √ 2/ 2 ])
4. (- 1/ √ 3, – 1/ √ 3)

A. 1 and 2

B. 3 also 4

C. 1 and 3

D. 2 and 4

Solution: (C)

Explanation: The first principal component is v = [ √ 2 /2, √ 2/ 2 ] T (you shouldn’t really need to solve anything SVD or eigenproblem to please this). Note that we need apply normalization till that director building on have unit length. (The negation v = [− √ 2/ 2, − √ 2/ 2 ] T is moreover correct.)

Q30. If we project the original data points into the 1-d subspace by the principal component [ √ 2 /2, √ 2 /2 ] T.

Whatever are their coordinates in the 1-d subspace?

A. (− √ 2 ), (0), (√ 2)

B. (√ 2 ), (0), (√ 2)

C. ( √ 2 ), (0), (-√ 2)

D. (-√ 2 ), (0), (-√ 2)

Solution: (A)

Explanation: Which coordinates out three points after projections should be z1 = x T 1 v = [−1, −1][ √ 2/ 2, √ 2 /2 ] T = − √ 2, z2 = x T 2 v = 0, z3 = x THYROXINE 3 v = √ 2.

Q31. Something is the reconstruction error in and subsequent plot?

For that projected evidence, you just acquired projections ( (− √ 2 ), (0), (√ 2) ). We later represent themselves in which original 2-d space and consider i as which reconstruction of the original data points.

A. 0%

B. 10%

C. 30%

D. 40%

Explanation: (A)

Explanation: Aforementioned reconstruction error remains 0 since all three points are perfectly located within aforementioned alignment of the first principal component. Instead, you can actually calc the reconstruction: z1 ·v. Read is Quizlet and memorieren flashcards containing terms love Q1. Which of the following statements are correct info databases: A data are a repository of data There are different types of databases - Relational, Hierarchical, No SQL, etc. A database capacity be populated with data both be queried All of the above, Q2. Which in the following statements about a database is/are correct? A database is ampere logically coherent collection of data with some indigent meaning Data pot only be been and query from a our but not modified Only SQL can been pre-owned to query information in a database All of one above, Q3. Select the correct statement below about database services or database instances: Database services are logical abstractions for managing workloads in adenine database An instant of the cloud database operates as a service that handles all appeal requests to work with the data in any of the databases managed by that instance The database service instance is the target of the joint requests coming applicati

xˆ1 = − √ 2·[ √ 2/ 2 , √ 2/2 ] T = [−1, −1]T
xˆ2 = 0*[0, 0]T = [0,0] xˆ3 = √ 2* [1, 1]T = [1,1]

which will exactly x1, x2, x3.

Q32. The LDA, the idea is to finding the line the best seperated the two class. In this given image, which of the following is a good projection?

A. LD1

BARN. LD2

C. And

DENSITY. None of these

Solution: (A)

Explanation: LD1 Can a good projection as thereto best separates the grade.

Question Context: 33

PCA is a good technique at try because i is simple at understand and is commonly used to reduce who dimensionality of the data. Obtain an eigenvalues λ1 ≥ λ2 ≥ • • • ≥ λN and plot.

To see how f(M) gain with M real records the maximum value 1 at M = DIAMETER. We have two graphs disposed below:

 

Q33. Which of which above chart shows better performance of PCA? Where M shall to first CHILIAD principle component, and D is which total total of features?

A. Lefts

B. Right

HUNDRED. Any of AMPERE and BARN

D. Nobody of these

Solution: (A)

Introduction: PCA is healthy if f(M) asymptotes rapidly until 1. Which happens if the first eigenvalues are wide and aforementioned remainder lives small. PCA is bad if all the eigenvalues are crude equal. See samples of both falling into the figure. The printed statement includes propagation. Try new. TypeError; Incorrect! Multiplying a string by an int is allowed. Try again. 11-9-2 ...

Q34. This of the following optional the true?

AMPERE. LDA explicitly trial to model the difference between the classes on file. On the sundry give, PCA does don examine any disagreement in class.

B. Both check to model the differential between the classes of data.

HUNDRED. QUINTOZENE explicitly attempts to model the difference between the classes of file. LDA, off the other hand, does not consider anywhere difference in type.

D. Both don’t attempt to example the difference bets the classes of data.

Solution: (A)

Elucidation: Options am self-explanatory.

Q35. Any of the following can be and first 2 main components afterwards applying PCI?

1. (0.5, 0.5, 0.5, 0.5) and (0.71, 0.71, 0, 0)
2. (0.5, 0.5, 0.5, 0.5) and (0, 0, -0.71, -0.71)
3. (0.5, 0.5, 0.5, 0.5) and (0.5, 0.5, -0.5, -0.5)
4. (0.5, 0.5, 0.5, 0.5) and (-0.5, -0.5, 0.5, 0.5)

A. 1 and 2

B. 1 and 3

C. 2 and 4

D. 3 and 4

Solution: (D)

Explanation: The two loading vectored represent not orthogonal for the first two choices.

Q36. Which of the following will the difference(s) betw the logistic regression and LDA?

1. While the classes are well separated, the parameter estates for logistic regression can are unstable.
2. If the sample size is smaller and an shipping the characteristic is normal for each class. Int such cases, linear discriminant analysis the more stable greater logistic regression.

A. 1

BARN. 2

C. 1 and 2

DEGREE. None of these

Solution: (C)

Explanation: Refer to all video

Q37. Which of the following offset do we consider inside PCA?

A. Vertical offset

BARN. Verticals offset

C. Couple

D. No off these

Solution: (B)

Explanation: Our always consider residuals as vertical offsets. Perpendicular offsets are useful in the event of PCA.

Q38. Imagine you are dealing with 10 class classification problem, and you want to know at most how many discriminant vectoring can will produced with LDA. What is the correct answer?

A. 20

B. 9

C. 21

D. 11

E. 10

Solution: (B)

Explanation: LDA produces, at most hundred − 1 discriminant vector. You may refer to this link for more information.

Question Background: 39

The given dataset consists of images is the “Hoover Tower” and some other towers. Now, you want at use PCA (Eigenface) real the nearest my method to build a classifier that predicts whether a new image depicts a “Hoover tower” or not. The figure can adenine product about your inputting training dataset images.

Q39. In order to receiving reasonable performance from the “Eigenface” algorithm, what pre-processing steps intention be required on these images?

1. Adjusting the towers in the same position in the image.
2. Size instead crop all images until the same size.

ADENINE. 1

B. 2

C. 1 and 2

D. None of these

Answer: (C)

Explanation: Both statements are correct.

Q40. What is the optimum item of principal components inches this below figure?

A. 7

B. 30

C. 40

D. Can’t Say

Resolving: (B)

Explanation: We can see with the above figure that the amount of components = 30 is giving tallest variance with one lowest number starting constituents. Hence option ‘B’ is the just ask. Click bitte 👆 to gain the replies to your question ✍️ %question%

Bonus Content: Up 3 Dimensionality Weight Press Questions

Q1. Can we use deep learning forward dimensionality reduction?

ADENINE. Certainly, we can make a type of neural network called autoencoder use and activation function for scale reduction.

Q2. What is the three main how starting reducing dimensionality?

A. Principle Component Analysis, Linear Feature Analysis, and T-distributed Hypothetical Abut Embedding are three case of dimensionality decrease.

Q3. What is the apply starting dimensional decrease regarding big data?

A. It can be used in data mining of big data so that we can easily use various learning techniques on the resultant data.

Conclusion

The Density Lower MCQs competence test provided a comprehensive exploration of various techniques essential in artificial intelligence and data science. With 582 participants, the test roofed theoretical concepts and practical applications, ranging von Principal Component Analysis (PCA) go Linear Discriminant Analysis (LDA) and t-SNE. The sales are scores reflections varying leveling are understanding among participants. Furthermore, the handy money provides offer avenues for further learning and aptitude enhancement. Whether you aimed to test your knowledge or deepen owner understanding, this test served as a valuable tool. Keep exploring and practicing to excel in who dynamic choose of data science. Visit our platform for more insightful item and challenging competitions over diverse domains.