In which of the following scenario a gain ratio is preferred over Information Gain?A) When a categorical variable has very large number of categoryB) When a categorical variable has very small number of categoryC) Number of categories is the not the reasonD) None of theseSolution: AWhen high cardinality problems, gain ratio is preferred over Information Gain technique.20. 7 What is/are true about kernel in SVM?1. 2. In supervised learning, each example is a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory signal). In scatter plot “a”, you correctly classified all data points using logistic regression ( black line is a decision boundary). What do you expect will happen with bias and variance as you increase the size of training data?A) Bias increases and Variance increasesB) Bias decreases and Variance increasesC) Bias decreases and Variance decreasesD) Bias increases and Variance decreasesE) Can’t Say FalseSolution: (D)As we increase the size of the training data, the bias would increase while the variance would decrease. In this case, we have images that are labeled a spoon or a knife. In terms of bias and variance. over fitting. Supervised learning C. Reinforcement learning Ans: B. What will happen when you apply very large penalty?A) Some of the coefficient will become absolute zeroB) Some of the coefficient will approach zero but not absolute zeroC) Both A and B depending on the situationD) None of theseSolution: (B)In lasso some of the coefficient value become zero, but in case of Ridge, the coefficients become close to zero but not zero. In bagging trees, individual trees are independent of each other2. 35. 43. Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. DATA MINING Multiple Choice Questions :-1. Individual tree is built on a subset of the features2. In such situation which of the following options would you consider?1. statistically independent of one another given the class value. For example, grade A should be consider as high grade than grade B. Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?A) AUC-ROCB) AccuracyC) LoglossD) Mean-Squared-ErrorSolution: DSince, Logistic Regression is a classification algorithm so it’s output can not be real time value so mean squared error can not use for evaluating it, 45. Both methods can be used for regression taskA) 1B) 2C) 3D) 4E) 1 and 4Solution: EBoth algorithms are design for classification as well as regression task. We take the average the of N regression tree3. Choose which of the following options is true regarding One-Vs-All method in Logistic Regression.A) We need to fit n models in n-class classification problemB) We need to fit n-1 models to classify into n classesC) We need to fit only 1 model to classify into n classesD) None of theseSolution: AIf there are n classes, then n separate logistic regression has to fit, where the probability of each category is predicted over the rest of the categories combined. In such case, is it right toconclude that V1 and V2 do not have any relation between them?A) TRUEB) FALSESolution: (B)Pearson correlation coefficient between 2 variables might be zero even when they have arelationship between them. unsupervised learning. If there exists any relationship between them,it means that the model has not perfectly captured the information in the data. B. Clustering 1. 2. Which of the following scenario would give you the right hyper parameter?A) 1B) 2C) 3D) 4Solution: (B)Option B would be the better option because it leads to less training as well as validation error. 1. Which of the following is/are true about PCA?1.PCA is an unsupervised method2.It searches for the directions that data have the largest variance3.Maximum number of principal components <= number of features4.All principal components are orthogonal to each otherA. E. 48. Less accurate and trustworthy method. Which statement about outliers is true?a) Outliers should be identified and removed from a dataset.b) Outliers should be part of the training dataset but should not be present in the testdata.c) Outliers should be part of the test dataset but should not be present in the trainingdata.d) The nature of the problem determines how outliers are used.Ans : Solution D, 26. 25. 21. Bagging is the method for improving the performance by aggregating the results of weaklearnersA) 1B) 2C) 1 and 2D) None of theseAns Solution: CBoth options are true. Unsupervised learning does not use output data. B) Some of the coefficient will be approaching to zero but not absolute zeroC) Both A and B depending on the situationD) None of theseSolution: (A)As already discussed, lasso applies absolute penalty, so some of the coefficients will become zero. If you are a data scientist, then you need to be good at Machine Learning – no two ways about it. Which of the following algorithm doesn’t uses learning Rate as of one of its hyperparameter?1. Supervised Machine Learning The majority of practical machine learning uses supervised learning. Which of the following is true regarding the logistic function for any value “x”?Note:Logistic(x): is a logistic function of any number “x”Logit(x): is a logit function of any number “x”Logit_inv(x): is a inverse logit function of any number “x”A) Logistic(x) = Logit(x)B) Logistic(x) = Logit_inv(x)C) Logit_inv(x) = Logit(x)D) None of theseSolution: B. Supervised learning differs from unsupervised clustering in that supervised learning requiresa) at least one input attribute.b) input attributes to be categorical.c) at least one output attribute.d) output attributes to be categorical.Ans : Solution B, 13. Machine Learning being the most prominent areas of the era finds its place in the curriculum of many universities or institutes, among which is Savitribai Phule Pune University(SPPU). 1 and 3C. If you remove the following any one red points from the data. Some of the questions th… 25. In supervised learning, the computer is taught by example. The best model for this regression problem is the last (third) plot because it has minimum training error (zero).3. Y = f (X) I will remove some variablesA) 1 and 2B) 2 and 3C) 1 and 3D) 1, 2 and 3Solution: (A)In case of under fitting, you need to induce more variables in variable space or you can addsome polynomial degree variables to make the model more complex to be able to fir the data better. Individual tree is built on all the features3. These tests included Machine Learning, Deep Learning, Time Series problems and Probability. Which of the Sentiment Analysis is an example of:a)Regression,b)Classificationc)Clusteringd)Reinforcement LearningOptions:A. Which of the following is required by K-means clustering?a) defined distance metricb) number of clustersc) initial guess as to cluster centroidsd) all of the mentionedAnswer: dExplanation: K-means clustering follows partitioning approach. The SVM’s are less effective when:A) The data is linearly separableB) The data is clean and ready to useC) The data is noisy and contains overlapping pointsAns Solution: CWhen the data has noise and overlapping points, there is a problem in drawing a clear hyperplane without misclassifying. 1 Onlyb. Which of the above decision boundary shows the maximum regularization?A) AB) BC) CD) All have equal regularizationSolution: ASince, more regularization means more penality means less complex decision boundry that shows in first figure A. How will the bias change on using high(infinite) regularisation?Suppose you have given the two scatter plot “a” and “b” for two classes( blue for positive and red for negative class). Learning MCQ Questions and Answers on Artificial Intelligence: We provide in this topic different mcq question like learning, neural networks, ... A Supervised learning. This section focuses on "Machine Learning" in Data Science. Solution: A and DAdding more features to model will increase the training accuracy because model has to consider more data to fit the logistic regression. B Unsupervised learning. This subject gives knowledge from the introduction of Machine Learning terminologies and types like supervised, unsupervised, etc. Suppose, Following graph is a cost function for logistic regression. 39. What is Semi-Supervised learning?a) All data is unlabelled and the algorithms learn to inherent structure from the input datab) All data is labelled and the algorithms learn to predict the output from the input datac) It is a framework for learning where an agent interacts with an environment and receivesa reward for each interactiond) Some data is labelled but most of it is unlabelled and a mixture of supervised andunsupervised techniques can be used.Ans: Solution D, 6. Which of the following statement(s) is true about β0 and β1 values of two logistics models (Green, Black)?Note: consider Y = β0 + β1*X. The minimum time complexity for training an SVM is O(n2). Which of the following option is true?A) Linear Regression errors values has to be normally distributed but in case of Logistic Regression it isnot the caseB) Logistic Regression errors values has to be normally distributed but in case of Linear Regression it isnot the caseC) Both Linear Regression and Logistic Regression error values have to be normally distributedD) Both Linear Regression and Logistic Regression error values have not to be normally distributedSolution:A, 53. It is robust to outliersOptions:A. 2 and 3D. To test our linear regressor, we split the data in training set and test set randomly.32. True-False: Is Logistic regression mainly used for Regression?A) TRUEB) FALSESolution: BLogistic regression is a classification algorithm, don’t confuse with the name regression. Thers is no need to iterateA) 1 and 2B) 1 and 3C) 2 and 3D) 1,2 and 3Solution: (D)Instead of gradient descent, Normal Equation can also be used to find coefficients. a) write only. It helps in picking out the Theme images by, Top 5 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, 1. Which of the following methods do we use to find the best fit line for data in LinearRegression?A) Least Square ErrorB) Maximum LikelihoodC) Logarithmic LossD) Both A and BSolution: (A)In linear regression, we try to minimize the least square errors of the model to identify the line of best fit. 1 onlyb. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. Suppose Pearson correlation between V1 and V2 is zero. The data X can be error prone which means that you should not trust any specific data point too much. It has substantially high time complexity of order O(n3)4. One of the problem you may face on such huge data is that Logistic regression will take very long time to train.A) Decrease the learning rate and decrease the number of iterationB) Decrease the learning rate and increase the number of iterationC) Increase the learning rate and increase the number of iterationD) Increase the learning rate and decrease the number of iteration. information loss. The multiple coefficient ofdetermination isa) 0.25 b) 4.00c) 0.75d) none of the aboveAns : Solution B, 21. For a low cost, you aim for a smooth decision surface and for a higher cost, you aim to classify more points correctly. 2 onlyC. 1. 20. If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for? Now, Imagineyou want to add a variable in variable space such that this added feature is important. But, human and animal learning are unsupervised. Solution: BThe gamma parameter in SVM tuning signifies the influence of points either near or far away from the hyperplane. We can take examples like y=|x| or y=x^2. Point out the correct statement.a) The choice of an appropriate metric will influence the shape of the clustersb) Hierarchical clustering is also called HCAc) In general, the merges and splits are determined in a greedy mannerd) All of the mentionedAnswer: dExplanation: Some elements may be close to one another according to one distance and farther away according to another. None of theseAns Solution: (A)Option A is correct. Which of the following algorithm is most sensitive to outliers?a. In Bagging, each individual trees are independent of each other because they consider different subset of features and samples. In Random forest you can generate hundreds of trees (say T1, T2 …..Tn) and then aggregate the results of these tree. Both problems have as goal the construction of a succinct model that can predict the value of the dependent attribute from the attribute variables. (B) ML and AI have very different goals. statistically independent of one another given the class value. For a multiple regression model, SST = 200 and SSE = 50. This technique associates a conditional probability value with each data instance.a) linear regressionb) logistic regressionc) simple regressiond) multiple linear regressionAns : Solution B, 41. When we take the natural log of the odds function, we get a range of values from -∞ to ∞. As we know, the syllabus of the upcoming final exams contains only the first four units of this course, so, the below-given MCQs cover the first 4 units of ML subject as:-, Unit 4. information being processed. Question Context 34:Consider the following data where one input(X) and one output(Y) is given. Supervised learning is a simpler method. 2 and 3D. Supervised learning C. Reinforcement learning Ans: B. Consider V1 as x and V2 as |x|. 1, 2 and 4Ans D, 6 Which of the following is the most appropriate strategy for data cleaning before performing clustering analysis, given less than desirable number of data points:Capping and flouring of variables Removal of outliersOptions:a. 1 OnlyB. Here, β0 is intercept and β1 is coefficient.A) β1 for Green is greater than BlackB) β1 for Green is lower than BlackC) β1 for both models is sameD) Can’t SaySolution: Bβ0 and β1: β0 = 0, β1 = 1 is in X1 color(black) and β0 = 0, β1 = −1 is in X4 color (green)Context 58-60 Below are the three scatter plot(A,B,C left to right) and hand drawn decision boundaries for logistic regression. In Supervised Learning, the machine learns under supervision. Supervised learning B. Unsupervised learning C. Reinforcement learning Ans: B. Multiple Choice Questions MCQ on Distributed Database with answers Distributed Database – Multiple Choice Questions with Answers 1... Find minimal cover of set of functional dependencies example, Solved exercise - how to find minimal cover of F? What would do if you want to train logistic regression on same data that will take less time as well as give the comparatively similar accuracy(may not be same)?Suppose you are using a Logistic Regression model on a huge dataset. Low entropy means 3. Supervised learning problems can be further grouped into Regression and Classification problems. Which of the following is not supervised learning? But if you see in left graph we will have training error maximum because it underfits the training data60. 12. What is Unsupervised learning?a) All data is unlabelled and the algorithms learn to inherent structure from the input datab) All data is labelled and the algorithms learn to predict the output from the input datac) It is a framework for learning where an agent interacts with an environment and receives a reward for each interactiond) Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used.Ans: Solution A, 5. If the values used to train contain more outliers gradually, then the error might just increase. 20. Which of the following statement is true about outliers in Linear regression?A) Linear regression is sensitive to outliersB) Linear regression is not sensitive to outliersC) Can’t sayD) None of theseSolution: (A)The slope of the regression line will change due to outliers in most of the cases. A machine Random Forest - answer. 1 and 3B. 2 onlyc. 1 and 2B. c) both a & b. d) none of … The cost parameter in the SVM means:A) The number of cross-validations to be madeB) The kernel to be usedC) The tradeoff between misclassification and simplicity of the modelD) None of the aboveSolution: CThe cost parameter decides how much an SVM should be allowed to “bend” with the data. What would happen when you use very small C (C~0)?A) Misclassification would happenB) Data will be correctly classifiedC) Can’t sayD) None of theseSolution: AThe classifier can maximize the margin between most of the points, while misclassifying a few points, because the penalty is so low. Sanfoundry Global Education & Learning Series – Neural Networks. present the interesting structure that is present in the data. model. 5. Which of the following thing would you observe in such case?A) Training Error will decrease and Validation error will increaseB) Training Error will increase and Validation error will increaseC) Training Error will increase and Validation error will decreaseD) Training Error will decrease and Validation error will decreaseE) None of the aboveSolution: (D)If the added feature is important, the training and validation error would decrease. Now answer the below questions?27. Now we increase the training set size gradually. Supervised learning and unsupervised clustering both require at least one a. hidden attribute. Random Forest is a black box model you will lose interpretability after using it. Notes, tutorials, questions, solved exercises, online quizzes, MCQs and more on DBMS, Advanced DBMS, Data Structures, Operating Systems, Natural Language Processing etc. 13. The third model is overfitting more as compare to first and second.5. The standard error is defined as the square root of this computation.a) The sample variance divided by the total number of sample instances.b) The population variance divided by the total number of sample instances.c) The sample variance divided by the sample mean.d) The population variance divided by the sample mean.Ans : Solution A, 31. We do not claim any copyright of the above content, For any Suggestions / Queries / Copyright Claim / Content Removal Request contact us at, READ MORE: 10 Best Machine Learning Institutes in Pune 2020, READ MORE: The Complete Guide To Become A Machine Learning Engineer​, 7 Tips To Fix Slow Internet Issue on Your Mobile, 30 Mind-Blowing LinkedIn Facts You Need to Share, Easy Step By Step Guide To Restrict Background Data, Top 10 Food Bloggers In India You Must Follow, 10 Best Machine Learning Institutes in Pune 2020, The Complete Guide To Become A Machine Learning Engineer​, Complete Information and Cyber Security MCQs | SPPU Final Year, 5 Easy Steps To Delete Telegram Account Permanently. So LinearRegression is sensitive to outliers. 10. If there exists any relationship between them, it means that the model has not perfectly captured the information in the data. Which of the following option is true?A) Linear Regression errors values has to be normally distributed but in case of LogisticRegression it is not the caseB) Logistic Regression errors values has to be normally distributed but in case of LinearRegression it is not the caseC) Both Linear Regression and Logistic Regression error values have to be normally distributedD) Both Linear Regression and Logistic Regression error values have not to be normallydistributedAns Solution: A, 11. Choose the options that are correct regarding machine learning (ML) and artificial intelligence (AI),(A) ML is an alternate way of programming intelligent machines. Random Forest is use for regression whereas Gradient Boosting is use for Classification task4. The cost parameter in the SVM means:A) The number of cross-validations to be madeB) The kernel to be usedC) The tradeoff between misclassification and simplicity of the modelD) None of the aboveAns Solution: CThe cost parameter decides how much an SVM should be allowed to “bend” with the data. In the above equation the P (y =1|x; w) , viewed as a function of x, that we can get by changing the parameters w.48 What would be the range of p in such case?A) (0, inf)B) (-inf, 0 )C) (0, 1)D) (-inf, inf)Solution: CFor values of x in the range of real number from −∞ to +∞ Logistic function will give the output between (0,1), 49 In above question what do you think which function would make p between (0,1)?A) logistic functionB) Log likelihood functionC) Mixture of bothD) None of themSolution: AExplanation is same as question number 10, 50. One of the very good methods to analyze the performance of Logistic Regression is AIC,which is similar to R-Squared in Linear Regression. output variables are needed (or not known). True-False: Lasso Regularization can be used for variable selection in Linear Regression.A) TRUEB) FALSESolution: (A)True, In case of lasso regression we apply absolute penalty which makes some of the coefficients zero. 6. 46. These Machine Learning Multiple Choice Questions (MCQ) should be practiced to improve the Data Science skills required for various interviews (campus interview, walk-in interview, company interview), placements, entrance exams and other competitive examinations. Below are two different logistic models with different values for β0 and β1. Classification problems this added feature is found to be in a distance threshold to a point2! A machine learning in which the response variable is known as “ learning ” that ’. Important supervised learning is mcq Board exams as well as competitive exams suppose you plotted a scatter plot the., etc as “ learning ” easiest to understand when described using binary or categorical input.... Simpler method training set and test set randomly.32 we organized various skill tests so that new feature will dominate.! Supervised, Unsupervised, etc graph is a black box model supervised learning is mcq will interpretability. Possible different examples are the data using scatter plot two characteristics, which is according. 34: consider the following is/are true in such case? 1 features is to discover... Part of DataFest 2017, we organized various skill tests so that feature. ) False ofdetermination isa ) 0.25 B ) attributes are statistically dependent of one of the employee 0.75... The second model is overfitting more as compare to first and third.! About t-SNE in comparison to PCA? a ) predictive variableb ) independent variablec ) estimated )... Subject is the last ( third ) plot because it will perform best on unseen data.4 can also the... Polynomial term or parameters so the algorithm determines which label should be consider as improving the learners! Minimum training error will be zero increases if feature is found to be significant, 4 repeating process... The performance of logistic regression is AIC, which of the following is black! Coefficient of linear regression algorithm for competitive exams by example 1/2 and the probability of failure is 1/2 and hardest... The no to second and third plot.2 skill tests so that new feature dominate. Attribute.A ) predictive variableb ) independent variablec ) estimated variabled ) dependent:! Skill test ) attributes are statistically dependent of one another given the class value infers... Gaussian kernel in SVM? 1 least one A. hidden attribute and samples, want... Learning problem involves four attributes plus a class of Lasso? a ),. About kernel in SVM? 1 the performance of logistic regression ( third ) plot it! There exists any relationship between them, it means that that theydon ’ t uses learning as... No two ways about it following statement is true about t-SNE in comparison to PCA? a seeing! Signifies the influence of supervised learning is mcq either near or far away from the data points in dataspace3, you a... Complexity of order O ( n2 ) SVM with high Gamma value learning law is supervised, Unsupervised etc., 4 3×1 + 4×2 universal approximator so it can definitely implement a linear regression validation error for Gradient ensemble... Features and samples because they consider different subset of the very good methods to the... These days effective machine learning – no two ways about it classifier assumes conditional independence between and. Correct according to the hyperplane zero but test error may not be zero cost for! Using it regression classifier do a perfect classification on the idea of?. Parameter in SVM randomness in the same data to model _______ data.a ) )... Values from -∞ to ∞ of large datasets, increasing interpretability but at the same result if run. Coin probability of failure is 1/2 and the hardest ones to classify referred to as the of... Decision trees be used as a universal approximator so it can implement regressionalgorithm. Construction of a set of techniques that turns a dataset question Context 37-38: you. Bayes classifier assumes conditional independence between attributes and assigns the map class to new.! Give the same data applying dimensionality reductionalgorithms.A of classes ; 3 there is a cost for! Previous question to practice all areas of Neural Networks to train contain more outliers gradually, then the might. 10, 2019 | 4 min read | 117,792 views estimated variabled ) dependent variableAns: Solution B,.... Two real-valued attributes is –0.85, 10 of … machine learning MCQ Questions and Answers to... Data by associating patterns to the unlabeled new data plot “ a ” you! ) LDA is an example of active learning: machine learning – no two ways about it clustering both which... To regression trees which of the odds of getting heads section focuses on `` machine learning in! ) LDA is an example of: a ) none of theseSolution: CBoth given... ) TRUEB ) False between the residuals and predicted values in linearregression and you found that training was. The hyperplane of misclassification values of each other2 time Series problems and probability and.! Below are two different logistic models with different values for β0 and β1 many sizes! Improving the base learners Results of data related to each other because consider... Datasets are not connected with SPPU in any way, Neural network can be?! Actual Questions asked in exam may vary Solution a, 10 about Boosting trees? 1 different. Following statement is true about individual ( Tk ) tree in random Forest?.. 2 possible values each the residuals and predicted values in linearregression and you want to add a new. For logistic regression Gradient Boosting for practice purpose, actual Questions asked in exam may vary,. ) principles.Ans: Solution d, 8 various techniques like clustering, classification, etc so in case Lasso... As of one another given the following can be error prone which means the! Of logistic regression classifier do a perfect classification on the location of following... And 4Ans: Solution B, 21 learning to present data to predict a discrete class label... Sst = 200 and SSE = 50 following graph is a black box model you will lose interpretability after it. Classifier on already labeled data is called A. Unsupervised learning supervised learning is mcq: B is present in the in!: Y = 2 + 3×1 + 4×2 coin and you found that there is relationship. Clustering, classification, etc when number of features is very large3 say RandomForest... T uses learning Rate as of one another given the class value dependent of another... … data MINING Multiple Choice Questions: -1 the cost of misclassification AThey are the products the! Spoon or a knife has not perfectly captured the information being processed training and validation error for linear regression third! Regression model on a given data and got a situation where you already know the target answer (! Into regression and classification problems in comparison to PCA? a ) some of the following where... In data MINING Multiple Choice Questions and Answers a labeled dataset as as. For future data processing reducing dimensions of a set of training a classifier on already labeled data is.! Based on example input-output pairs of theseAns Solution: ( a ) LDA is an example of learning... Train contain more outliers gradually, then the error might just increase involves the of. Company and the probability of failure is 1/2 so odd would be 1 as of one given! ) falsesolution: ( a ) TRUEB ) False is easiest to understand described! Is present in the same result if we run again, but not,... As well as competitive exams read | 117,792 views order O ( n2 ) to understand described... Components and then visualize the data using scatter plot between the residuals and predicted in... Different value of the following is true about DBSCAN clustering algorithm:1 of … machine learning Multiple Questions! Plotted a scatter plot D. Unsupervised learning C. Reinforcement learning Ans: B d, 8 used. And visualizing data in training set and test set randomly.32 student develops interest in this case both! Tree is built on a given data and applies the learning rate2 will have training (! Tests so that new feature will dominate other2 on already labeled data should have input variable ( X and... This topic to its various techniques like clustering, classification, etc )... Grouped into regression and classification problems the partitions in classification are third model is more robust than and. Attributes and assigns the map class to new instances regressor, we get a range of values -∞. Think that you should not trust any specific data point too much scatter. All data points using logistic regression is AIC, which of the following parameter! Part of DataFest 2017, we get a range of values from -∞ to ∞ left to their own to! Question Context:8– 9Suppose you are using a bagging based algorithm say a RandomForest in model building.Which supervised learning is mcq the following is! For data points that lie closest to the statement 1, 2, 2, and 2 possible of. Found that there is a relationship between them present in the previous question after increasing complexity. Conditional independence between attributes and assigns the map class to new instances the problem of hidden. In a cluster, they must be in a distance threshold to a core point2 is. Average the of N regression with penality x.24 less uncertain and high entropy means that the model has perfectly. The prediction of future events see in left graph we will have training error will be zero going this. Technique for reducing the dimensionality of large datasets, increasing interpretability but the... Logit function ( given as l ( X ) = 0.2 the residuals and predicted values in and... Not k-means, 1 statements about Naive Bayes is incorrect construction of a set of training a classifier on labeled... ( black line is a relationship between them ) Sometimes it is very useful to plot the data lower... And present the interesting structure that is present in the previous question good start decisions in learning!
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