**PRINCIPAL COMPONENT ANALYSIS **Machine Learning Multiple Choice Questions and answers pdf is very important for Various examination of University and Colleges these questions will definitely help you for your studies.

In this post we have coverd up Function Analysis multiple choice questions, Logistic Regression multiple choice questions, Independent component analysis multiple choice questions,Dimensionality Reduction all topics multiple choice questions and answers, feature selection multiple choice questions and answers.

### 1.________is a tool which is used to reduce the dimension of the data.

A.Principal components analysis

B.Product Components analysis

C.Principle Components analysis

D.Pre Complex analysis

**Ans : A**

**2.PCA reduces the dimension by finding a few________.**

A.

**Hexagonal linear combination**B. Orthogonal linear combinations

C. Octagonal linear combination

D. Pentagonal Linear Combination

**Ans : B**

**3.PCA is a ________.**

A. Non linear method

B. Linear method

C. Continuous method

D. Repeated method

**Ans : B**

**4.Which of the following is not kernel method? **

A. linear

B. polynomial

C. gaussian

D. Continuous

**Ans. D**

### 5.PCA is used to find __________.

A. Relationship between components

B. Linear regression

C. Linear relation

D. Inter relation

**Ans : D**

### 6. _________ is non-zero vector that stays parallel after matrix multiplication.

A. Eigen value

B. Eigen vector

C. Linear value

D. None of these

**Ans. B**

**7. ________ basically known as characteristic roots. It basically measures the variance in all variables which is accounted for by that factor**

A. Eigen value

B. Eigen vector

C. Linear value

D. None of these

**Ans. A**

**8. __________is a dimensionality reduction technique which is commonly used for the supervised classification problems. **

A. Value analysis

B. Function Analysis

C. Pure analysis

D. None of these

**Ans : B**

**9. There are _____ types of Supervised Learning algorithms used for classification in Machine Learning. **

A. 2

B. 3

C. 4

D. 5

**Ans. A**

**10. Discriminative Learning Algorithms include ________.**

A. Continuous regression

B. Logistic Regression

C. Linear regression

D. None of these

**Ans : B**

### 11.The predictions for generative learning algorithms are made using _______ .

A. Naive Theorem

B. Bayes Theorem

C. Naive Bayes Theorem

D. None of these

**Ans : B**

**12. _________is a Generative Learning Algorithm and in order to capture the distribution of each class. **

A. Naive Theorem

B. Bayes Theorem

C. Naive Bayes Theorem

D. Gaussian Discriminant Analysis

**Ans : D**

### 13. ___________ is a machine learning technique to separate independent sources from a mixed signal.

A. Naive Theorem

B. Independent component analysis

C. Naive Bayes Theorem

D. Gaussian Discriminant Analysis

**Ans : B**

**14. ICA stands for _____ **

A. Independent component analysis

B. Inactive component analysis

C. Intractive component analysis

D. Inactive component Automation

**Ans : A**

**15. __________is a step of Data Pre Processing which is applied to independent variables or features of data.**

A. Error finding

B. Standardization

C. Gradient descent

D. None of these

**Ans. B**

**16. ________is an important factor in predictive modeling**

A. Dimensionality Reduction

B. feature selection

C. feature extraction

D. None of these

**Ans. A**

**17. Feature selection has _____& different approaches**

A. 2

B. 3

C. 4

D. 5

**Ans. C**

**18._______approach has high computational complexity.**

A. Wrapper

B. Filter

C. Embedded

D. Hybrid

**Ans : A**

**19 ________approach first selects the possible optimal feature set which is further tested by the wrapper approach. **

A. Wrapper

B. Filter

C. Embedded

D. Hybrid

**Ans : D**

### 20.Parameters For Feature Selection are classified on ____ factors.

A. 3

B. 2

C. 4

D. 5

**Ans : 2**