**Machine Learning Axioms Fresco Play MCQs Answers**

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**Course Path: Data Science/MACHINE LEARNING METHODS/Machine Learning
Axioms**

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**Quiz on Supervised & Unsupervised Learning**

1.If you have a basket of different fruit varieties with some prior information on size, color, shape of each and every fruit . Which learning methodology is best applicable?

- Supervised Learning
- Unsupervised Learning

Answer: 1)Supervised Learning

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**Quiz on Decision Trees**

1.Do you think heuristic for rule learning and heuristics for decision trees are both same ?

- True
- False

Answer: 2)False

2.Now Can you make quick guess where Decision tree will fall into _____

- Supervised Learning
- Unsupervised Learning

Answer: 1)Supervised Learning

**Quiz on Naïve Bayes**

1.What is the benefit of Naïve Bayes ?

- Does not require any data
- can handle any data volume easily
- Requires less training data
- can process faster with any data

Answer: 3)Requires less training data

**Quiz - Gradient Descent**

1.What is the advantage of using an iterative algorithm like gradient descent ? (select the best)

- Linear regression problems there is no closed form solution
- For Nonlinear regression problems, there is no closed form solution
- Linear regression problems have multiple solutions

Answer: 2)For Nonlinear regression problems, there is no closed form solution

**Quiz - Linear Regression**

1.For which one of these relationships could we use a regression analysis? Choose the correct one

- Relationship between being part of committee and number of eye operations
- Relationship between Height & weight (both Quantitative)
- Relation between age and person is married
- Relationship between eye color (blue/black) and hair color (grey,blonde)

Answer: 2)Relationship between Height & weight (both Quantitative)

**Quiz - Logistic Regression**

1.Does Logistic regression check for the linear relationship between dependent and independent variables ?

- False
- True

Answer: 1)False

**Quiz on Support Vector Machine**

1.Which helps SVM to implement the algorithm in high dimensional space?

- Classification
- Kernel
- Logistic Regression
- Multi-Linear Regression

Answer: 2)Kernel

**Quiz - Kernel Methods**

1.Kernel methods can be used for supervised and unsupervised problems

- True
- False

Answer: 1)True

**Quiz - Neural Networks**

1.Perceptron is _______________

- an auto-associative neural network
- a single layer feed-forward neural network
- a double layer auto-associative neural network

Answer: 2)a single layer feed-forward neural network

**Quiz on Clustering**

1.While running the same algorithm multiple times, which algorithm produces same results?

- Classification Clustering
- K Means clustering
- Hierarchical clustering

Answer: 3)Hierarchical clustering

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**Machine Learning Final Assessment**

1.If the outcome is continuous, which model to be applied?

- Linear Regression
- Classification
- Multi-Linear Regression
- Logistic Regression

Answer: 1)Linear Regression

2.The model which is widely used for the classification is

- Logistic Regression
- Segmentation
- Linear Regression
- Multi-Linear Regression

Answer: 1)Logistic Regression

3.Which of them, best represents the property of Kernel?

- Modularity
- Scalability
- Converge
- Extensibility

Answer: 1)Modularity

4.SVM will not perform well with large data set because (select the best answer)

- training time is high
- Difficult to simulate model
- classification becomes difficult
- Lot of noise in data

Answer: 1)training time is high

5.What are different types of Supervised learning

- Naive Bayes & classification
- regression and classification
- Segmentation and regression
- Clustering and regression

Answer: 2)regression and classification

6.SVM uses which method for pattern analysis in High dimensional space?

- Multi-Linear Regression
- Kernel
- Logistic Regression
- Classification

Answer: 2)Kernel

7.Which type of the clustering could handle Big Data?

- Hierarchical clustering
- K Means clustering

Answer: 2)K Means clustering

8.Which of the following is not example of Clustering?

- Recommendation engines
- Image segmentation
- RFM Analysis
- Anomaly detection
- Market segmentation

Answer: 3)RFM Analysis

9.Most famous technique used in Text mining is

- Segmentation
- Clustering
- Naive Bayes

Answer: 3)Naive Bayes

10.The main problem with using single regression line

- Response variable is not appropriate
- Curvilinear data
- merging of groups
- presence of 1 or more outliers

Answer: 4)presence of 1 or more outliers

11.In Kernel trick method, We do not need the coordinates of the data in the feature space

- False
- True

Answer: 2)True

12.Effect of outlier on the correlation coefficient ______________

- decrease the correlation coefficient
- no effect on a correlation coefficient
- An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to the other points
- increase a correlation coefficient

Answer: 3)An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to the other points

13.Which model helps SVM to implement the algorithm in high dimensional space?

- Kernel
- Multi-Linear Regression
- Logistic Regression
- Classification

Answer: 1)Kernel

14.Which technique implicitly defines the class of possible patterns by introducing a notion of similarity between data?

- SVM
- Multi-Linear Regression
- Kernel
- Hierarchical clustering
- Linear Regression

Answer: 3)Kernel

15.Consider a regression equation, Now which of the following could not be answered by regression?

- Predict the value of y at a particular value of x
- Estimate whether the association is linear or non-linear
- Estimate whether the linear association is positive or negative
- Estimate the slope between y and x

Answer: 2)Estimate whether the association is linear or non-linear

16.The model in which one estimates the probability that the outcome variable assumes a certain value, rather than estimating the value itself.

- Multi-Linear Regression
- Logistic Regression
- Classification
- Linear Regression

Answer: 2)Logistic Regression

17.While running the same algorithm multiple times, which algorithm produces same results?

- K Means clustering
- Hierarchical clustering

Answer: 2)Hierarchical clustering

18.If the outcome is binary(0/1), which model to be applied?

- Classification
- Logistic Regression
- Multi-Linear Regression
- Linear Regression

Answer: 2)Logistic Regression

19.One has to run through ALL the samples in your training set to do a single update for a parameter in a particular iteration. This is applicable for

- Anomaly detection
- Gradient Descent
- stochastic gradient descent
- Neural Networks

Answer: 2)Gradient Descent

20.What are the advantages of neural networks (i) ability to learn by example (ii) fault tolerant (iii) suited for real time operation due to their high 'computational' rates

- All the options are correct
- (ii) and (iii) are true
- (i) and (ii) are true
- (i) and (iii) are true

Answer: 1)All the options are correct

21.Correlation and regression are concerned with the relationship between _________

- quantitative response variable and categorical explanatory variable
- 2 quantitative variables
- 2 categorical variables
- quantitative explanatory variable and categorical response variable

Answer: 2)2 quantitative variables

22.The correlation between two variables is given by r = 0.0. . This means

- There is a perfect positive relationship between the two variables
- The best straight line through the data is horizontal.
- All of the points must fall exactly on a horizontal straight line
- There is a perfect negative relationship between the two variables

Answer: 2)The best straight line through the data is horizontal.

23.Which clustering technique requires prior knowledge of the number of clusters required?

- Hierarchical clustering
- K Means clustering

Answer: 2)K Means clustering

24.Disadvantage of Neural network according to your purview is

- takes long time to be trained
- iterations should be defined
- More nodes to be defined

Answer: 1)takes long time to be trained

25.The standard approach to supervised learning is to split the set of example into the training set and the test

- True
- False

Answer: 1)True

26.Which methodology works with clear margins of separation points?

- Support Vector Machine
- Multi-Linear Regression
- Linear Regression
- Logistic Regression

Answer: 1)Support Vector Machine

27.Which of the learning methodology applies conditional probability of all the variables with respective the dependent variable?

- Unsupervised Learning
- Supervised Learning

Answer: 2)Supervised Learning

28.Objective of unsupervised data covers all these aspect except

- trace interesting directions in data
- prepare the training data set
- detect interesting coordinates and correlations
- find clusters of the data
- low-dimensional representations of the data

Answer: 2)prepare the training data set

29.SVM will not perform well with data with more noise because (select the best answer)

- more work involved in removing noise
- training time is high
- Difficult to simulate model
- target classes could overlap

Answer: 4)target classes could overlap

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