# Machine Learning Axioms MCQ Answers

Hello friends in this post we are going to discuss about Machine Learning Axioms TCS Fresco Play Dumps | Machine Learning Axioms TCS Fresco Play Answers | Machine Learning Axioms TCS Answers | Machine Learning Axioms Objective type question answer

Q1.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?
ANS – Supervised Learning

Q2.Do you think heuristic for rule learning and heuristics for decision trees are both same ?
False

Q3.What is the benefit of Naïve Bayes ?
ANS – Requires less training data

Q4.What is the advantage of using an iterative algorithm like gradient descent ? (select the best)
ANS – For Nonlinear regression problems, there is no closed form solution

Q5.For which one of these relationships could we use a regression analysis? Choose the correct one
ANS – Relationship between Height & weight (both Quantitative)

Q6.Does Logistic regression check for the linear relationship between dependent and independent variables ?
False

Q7.Which helps SVM to implement the algorithm in high dimensional space?
ANS – Kernel

Q8.Kernel methods can be used for supervised and unsupervised problems
True

Q9.Perceptron is _______
ANS – a single layer feed-forward neural network

Q10.While running the same algorithm multiple times, which algorithm produces same results?
ANS – Hierarchical clustering

Q11.SVM will not perform well with large data set because (select the best answer)
ANS – training time is high

Q12.In a scenario, where the statistical model describes random error or noise instead of underlying relationship, what happens
ANS – Overfitting

Q13.Consider a regression equation, Now which of the following could not be answered by regression?
ANS – Estimate whether the association is linear or non-linear

Q14.Now Can you make quick guess where Decision tree will fall into ______
ANS – Supervised Learning

Q15.The main difficulty with using a regression line to analyze these data is _____
ANS – presence of 1 or more outliers

Q16.For which one of these relationships could we use a regression analysis? Chose the correct one
ANS – Relationship between Height & weight (both Quantitative)

Q17.The correlation between two variables is given by r = 0.0. . This means
ANS – The best straight line through the data is horizontal.

Q18.Most famous technique used in Text mining is
ANS – Naive Bayes

ANS – takes long time to be trained

Q20.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

Q21.Which type of the clustering could handle Big Data?
ANS – K Means clustering

Q22.Effect of outlier on the correlation coefficient __
ANS – An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to the other points — Correct

Q23.If the outcome is continuous, which model to be applied?
ANS – Multi-Linear Regression

Q24.SVM uses which method for pattern analysis in High dimensional space?
ANS – Kernel

Q25.Correlation and regression are concerned with the relationship between _
ANS – 2 quantitative variables

Q26.Which model helps SVM to implement the algorithm in high dimensional space?
ANS – Kernel

Q27.In Kernel trick method, We do not need the coordinates of the data in the feature space
True

Q28.What are different types of Supervised learning
ANS – regression and classification

Q29.Which methodology works with clear margins of separation points?
ANS – Support Vector Machine

Q30.Which of the learning methodology applies conditional probability of all the variables with respective the dependent variable?
ANS – Supervised Learning

Q31.The main problem with using single regression line
ANS – presence of 1 or more outliers

Q32.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
ANS – All the options are correct

Q33.Which clustering technique requires prior knowledge of the number of clusters required?
ANS – K Means clustering

Q34.Which of them, best represents the property of Kernel?
ANS – Modularity

Q35.The model in which one estimates the probability that the outcome variable assumes a certain value, rather than estimating the value itself.
ANS – Logistic Regression

Q36.If the outcome is binary(0/1), which model to be applied?
ANS – Logistic Regression

Q37.SVM will not perform well with data with more noise because (select the best answer)
ANS – target classes could overlap

Q38.The standard approach to supervised learning is to split the set of example into the training set and the test
True