Machine Learning Axioms Fresco Play MCQs Answers

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?

1. Supervised Learning
2. 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 ?

1. True
2. False

Answer: 2)False

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

1. Supervised Learning
2. Unsupervised Learning

Answer: 1)Supervised Learning

Quiz on Naïve Bayes

1.What is the benefit of Naïve Bayes ?

1. Does not require any data
2. can handle any data volume easily
3. Requires less training data
4. 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)

1. Linear regression problems there is no closed form solution
2. For Nonlinear regression problems, there is no closed form solution
3. 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

1. Relationship between being part of committee and number of eye operations
2. Relationship between Height & weight (both Quantitative)
3. Relation between age and person is married
4. 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 ?

1. False
2. True

Answer: 1)False

Quiz on Support Vector Machine

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

1. Classification
2. Kernel
3. Logistic Regression
4. Multi-Linear Regression

Answer: 2)Kernel

Quiz - Kernel Methods

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

1. True
2. False

Answer: 1)True

Quiz - Neural Networks

1.Perceptron is _______________

1. an auto-associative neural network
2. a single layer feed-forward neural network
3. 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?

1. Classification Clustering
2. K Means clustering
3. 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?

1. Linear Regression
2. Classification
3. Multi-Linear Regression
4. Logistic Regression

Answer: 1)Linear Regression

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

1. Logistic Regression
2. Segmentation
3. Linear Regression
4. Multi-Linear Regression

Answer: 1)Logistic Regression

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

1. Modularity
2. Scalability
3. Converge
4. Extensibility

Answer: 1)Modularity

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

1. training time is high
2. Difficult to simulate model
3. classification becomes difficult
4. Lot of noise in data

Answer: 1)training time is high

5.What are different types of Supervised learning

1. Naive Bayes & classification
2. regression and classification
3. Segmentation and regression
4. Clustering and regression

Answer: 2)regression and classification

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

1. Multi-Linear Regression
2. Kernel
3. Logistic Regression
4. Classification

Answer: 2)Kernel

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

1. Hierarchical clustering
2. K Means clustering

Answer: 2)K Means clustering

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

1. Recommendation engines
2. Image segmentation
3. RFM Analysis
4. Anomaly detection
5. Market segmentation

Answer: 3)RFM Analysis

9.Most famous technique used in Text mining is

1. Segmentation
2. Clustering
3. Naive Bayes

Answer: 3)Naive Bayes

10.The main problem with using single regression line

1. Response variable is not appropriate
2. Curvilinear data
3. merging of groups
4. 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

1. False
2. True

Answer: 2)True

12.Effect of outlier on the correlation coefficient ______________

1. decrease the correlation coefficient
2. no effect on a correlation coefficient
3. An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to the other points
4. 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?

1. Kernel
2. Multi-Linear Regression
3. Logistic Regression
4. Classification

Answer: 1)Kernel

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

1. SVM
2. Multi-Linear Regression
3. Kernel
4. Hierarchical clustering
5. Linear Regression

Answer: 3)Kernel

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

1. Predict the value of y at a particular value of x
2. Estimate whether the association is linear or non-linear
3. Estimate whether the linear association is positive or negative
4. 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.

1. Multi-Linear Regression
2. Logistic Regression
3. Classification
4. Linear Regression

Answer: 2)Logistic Regression

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

1. K Means clustering
2. Hierarchical clustering

Answer: 2)Hierarchical clustering

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

1. Classification
2. Logistic Regression
3. Multi-Linear Regression
4. 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

1. Anomaly detection
2. Gradient Descent
3. stochastic gradient descent
4. 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

1. All the options are correct
2. (ii) and (iii) are true
3. (i) and (ii) are true
4. (i) and (iii) are true

Answer: 1)All the options are correct

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

1. quantitative response variable and categorical explanatory variable
2. 2 quantitative variables
3. 2 categorical variables
4. 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

1. There is a perfect positive relationship between the two variables
2. The best straight line through the data is horizontal.
3. All of the points must fall exactly on a horizontal straight line
4. 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?

1. Hierarchical clustering
2. K Means clustering

Answer: 2)K Means clustering

24.Disadvantage of Neural network according to your purview is

1. takes long time to be trained
2. iterations should be defined
3. 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

1. True
2. False

Answer: 1)True

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

1. Support Vector Machine
2. Multi-Linear Regression
3. Linear Regression
4. 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?

1. Unsupervised Learning
2. Supervised Learning

Answer: 2)Supervised Learning

28.Objective of unsupervised data covers all these aspect except

1. trace interesting directions in data
2. prepare the training data set
3. detect interesting coordinates and correlations
4. find clusters of the data
5. 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)

1. more work involved in removing noise
2. training time is high
3. Difficult to simulate model
4. target classes could overlap

Answer: 4)target classes could overlap

 

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