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linear regression machine learning exam questions

linear regression machine learning exam questions

Simple linear regression is a useful approach for predicting a response on the basis of a single predictor variable. 21) What will happen when you fit degree 2 polynomial in linear regression? Please feel free to share your thoughts. For more such skilltests, check out our current hackathons. So Linear Regression is sensitive to outliers. This page lists down the practice tests / interview questions and answers for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning.Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. C) A or B depend on the situation 3) True-False: It is possibl… (a)[1 point] We can get multiple local optimum solutions if we solve a linear regression problem by minimizing the sum of squared errors using gradient descent. There are 30 multiple choice questions worth 3 points each, and 6 written ... [3 pts] Lasso can be interpreted as least-squares linear regression where B) Relation between the X1 and Y is strong 24) Now we increase the training set size gradually. A Comprehensive Learning Path to Become a Data Scientist in 2021! A) Bias increases and Variance increases 10) Suppose Pearson correlation between V1 and V2 is zero. True. Scale is same in both graphs for both axis. D) Can’t Say. A Review of 2020 and Trends in 2021 – A Technical Overview of Machine Learning and Deep Learning! We need to consider the both of these two statements. E) None of the above. Do you want to master the concepts of Linear Regression and Machine Learning? D) None of these. Linear and Logistic regression are the most commonly used ML Algorithms. Which of the following is true about below graphs(A,B, C left to right) between the cost function and Number of iterations? 1) True-False: Linear Regression is a supervised machine learning algorithm. However, in practice we often have more than one predictor. Suppose we use a linear regression method to model this data. 2. var notice = document.getElementById("cptch_time_limit_notice_14"); D) Correlation can’t judge the relationship. Since a degree 2 polynomial will be less complex as compared to degree 3, the bias will be high and variance will be low. B) Maximum Likelihood D) None of these. Refer this article for read more about normal equation. Get sample data 3. 3) True-False: It is possible to design a Linear regression algorithm using a neural network? Should I become a data scientist (or a business analyst)? 1) View Solution Exam Questions - Regression | ExamSolutions Standard linear regression is an example of a generalized linear model where the response is normally distributed and the link is the identity function. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another terms it perfectly fits the data.”. Please reload the CAPTCHA. Below is the distribution of the scores of the participants: You can access the scores here. It is used to predict the relationship between a dependent variable and a … A Neural network can be used as a universal approximator, so it can definitely implement a linear regression algorithm. See Unit 4.4.1. As already discussed, lasso applies absolute penalty, so some of the coefficients will become zero. C) Relation between the X1 and Y is neutral Vitalflux.com is dedicated to help software engineers & data scientists get technology news, practice tests, tutorials in order to reskill / acquire newer skills from time-to-time. would look at person and predict if s/he has lack of Haemoglobin (red blood cells 8 Thoughts on How to Transition into Data Science from Different Backgrounds. B) There are high chances that degree 4 polynomial will under fit the data 22) In terms of bias and variance. B) l1 > l2 > l3 }. 3. We cannot comment on the correlation coefficient by using only statement 1. C) Remain constant C) 2 and 3 Train a machine learning model using the linear regression algorithm on the full dataset (all columns) housing_boston.csv with Python Scikit-Learn. D) Bias will be low, variance will be low. C) Logloss Time limit is exhausted. There should not be any relationship between predicted values and residuals. 3. Pearson correlation coefficient between 2 variables might be zero even when they have a relationship between them. What you are talking of id Polynomial Regression which we generally use in Machine Learning. 4) Which of the following methods do we use to find the best fit line for data in Linear Regression? Time limit is exhausted. setTimeout( Here is the leaderboard for the participants who took the test. D) None of these. False Sol: True. if ( notice ) C) Bias will be high, variance will be low Usually, in a data science interview, at least one or two questions can be expected on this topic. D) None of these. 6) True-False: Lasso Regularization can be used for variable selection in Linear Regression. 1) A machine learning team has several large CSV datasets in Amazon S3. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. We calculate the direct differences between actual value and the Y labels. D) None of these. B) Decrease A) There are high chances that degree 4 polynomial will over fit the data A) Least Square Error 26) What would be the root mean square training error for this data if you run a Linear Regression model of the form (Y = A0+A1X)? })(120000); If you are one of those who missed out on this skill test, here are the questions and solutions. A) AUC-ROC D) None of these. C) We can’t say about bias B) 2 and 3 A) Lower is better a machine learning approach. If there exists any relationship between them,it means that the model has not perfectly captured the information in the data. D) None of these, Sum of residuals will always be zero, therefore both have same sum of residuals. Includes the following steps: 1) Load the data. Following is the list of some good courses / pages: (adsbygoogle = window.adsbygoogle || []).push({}); (function( timeout ) { What is process of carrying out a linear regression? Classification vs Regression – Machine Learning Interview Questions – Edureka Which of the following thing would you observe in such case? C) Both, depending on the situation D) None of above. In case of under fitting, you need to induce more variables in variable space or you can add some polynomial degree variables to make the model more complex to be able to fir the data better. A) In case of very large x; bias is low I had thought MLE would be better for complex data. What can a machine learning specialist do to address this concern? In technical terms, linear regression is a machine learning algorithm that finds the best linear-fit relationship on any given data, between independent and dependent variables. If possible can you please post more question on Linear as well as Multiple regression and on Hypothesis theory as well. Explain Classification and Regression. 8. 2) True-False: Linear Regression is mainly used for Regression. The team’s leaders need to accelerate the training process. Know about the Machine Learning & how it work, Interview Questions, Machine Learning Resume Tips, Linear Regression and Random forest. Which of the following is/are true about Normal Equation? Explain the differences between Logistic and Linear regression? A) Linear regression is sensitive to outliers It was specially designed for you to test your knowledge on linear regression techniques. But one question, a degree 3 polynomial regression isn’t considered as a linear regerssion model right? True, In case of lasso regression we apply absolute penalty which makes some of the coefficients zero. In such case, is it right to conclude that V1 and V2 do not have any relation between them? Time: 80 minutes. 20) What will happen when you fit degree 4 polynomial in linear regression? B) Greater than zero X axis is independent variable and Y-axis is dependent variable. Therefore lower residuals are desired. B) Bias will be low, variance will be high C) Can’t say A) Less than 0 If you are one of those who missed out on this skill test, here are the questions and solutions. Suppose horizontal axis is independent variable and vertical axis is dependent variable. We can perfectly fit the line on the following data so mean error will be zero. Thank you for visiting our site today. C) Both have same sum of residuals A) Increase zero D) None of these.  −  Thanks for all these questions. A) 1 and 2 The correlation coefficient would not be close to 1 in such a case. We first convert the spreadsheet into a matrix. Deep Learning vs Machine Learning – Machine Learning Interview Questions – Edureka. .hide-if-no-js { In such case training error will be zero but test error may not be zero. B) 1 and 3 The main goal of regression is the construction of an efficient model to predict the dependent attributes from a bunch of attribute variables. If V1 decreases then V2 behavior is unknown, A) Pearson correlation will be close to 1 The correct answer is D. Lower Residuals SQUARES are better than higher residuals squares! 10-601 Machine Learning Midterm Exam October 18, 2012 Question 1. State the assumptions in a linear regression model. D) None of these. 5 Questions which can teach you Multiple Regression (with R and Python), Going Deeper into Regression Analysis with Assumptions, Plots & Solutions. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another terms it perfectly fits the data. machine learning quiz and MCQ questions with answers, data scientists interview, question and answers in clustering, naive bayes, supervised learning, high entropy in machine learning Advanced Database Management System - Tutorials and Notes: Machine Learning Multiple Choice Questions and Answers 01 }, Here are the definitions: Linear Regression - Linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). D) None of these. Consider the following data where one input(X) and one output(Y) is given. We don’t have to choose the learning rate, It becomes slow when number of features is very large. I would love to hear your feedback about the skilltest. Remaining options are use in case of a classification problem. This page lists down the practice tests / interview questions and answers for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. B) It is high chances that degree 2 polynomial will under fit the data Q4. More than 800 people took this test. Welcome to the second part of the series of commonly asked interview questions based on machine learning algorithms. 2 Multiple Linear Regression. • The exam is closed book, closed notes except your one-page (two sides) or two-page (one side) crib sheet. True b. Really helped. 29) In such situation which of the following options would you consider? Commonly used Machine Learning Algorithms (with Python and R Codes), 40 Questions to test a data scientist on Machine Learning [Solution: SkillPower – Machine Learning, DataFest 2017], Introductory guide on Linear Programming for (aspiring) data scientists, 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R, 30 Questions to test a data scientist on K-Nearest Neighbors (kNN) Algorithm, 16 Key Questions You Should Answer Before Transitioning into Data Science. If a degree 3 polynomial fits the data perfectly, it’s highly likely that a simpler model(degree 2 polynomial) might under fit the data. • Please use non-programmable calculators only. If you are given the two variables V1 and V2 and they are following below two characteristics. Are you a beginner in Machine Learning? Now, Imagine you want to add a variable in variable space such that this added feature is important. It is also one of the first methods people get their hands dirty on. We always consider residuals as vertical offsets. A) Since the there is a relationship means our model is not good As we increase the size of the training data, the bias would increase while the variance would decrease. 1. D) 1,2 and 3. Machine Learning: Supervised - Linear Regression. What is logistic regression? We request you to post this comment on Analytics Vidhya's, 30 Questions to test a data scientist on Linear Regression [Solution: Skilltest – Linear Regression], A) Pearson correlation will be close to 1. 10-701/15-781 Machine Learning - Midterm Exam, Fall 2010 Aarti Singh Carnegie Mellon University 1. 2) Preprocess the dataset. A) Bias will be high, variance will be high 12) True- False: Overfitting is more likely when you have huge amount of data to train? Can’t we use OLS or MLE to find best fit line in Linear Regression? You missed on the real time test, but can read this article to find out how many could have answered correctly. With a small training dataset, it’s easier to find a hypothesis to fit the training data exactly i.e. Thanks for making it possible to train our knowledge regarding regression techniques. D) Training Error will decrease and Validation error will decrease A) Relation between the X1 and Y is weak 7) Which of the following is true about Residuals ? 17) What will happen when you apply very large penalty in case of Lasso? In case of high learning rate, step will be high, the objective function will decrease quickly initially, but it will not find the global minima and objective function starts increasing after a few iterations. Maybe try out some linear model (Ridge or Lasso) and compare it to a more complex model? B) Pearson correlation will be close to -1 If you are not sure of your answer you may wish to provide a brief explanation. Suppose you have been given the following scenario for training and validation error for Linear Regression. I have written below python code: ... Browse other questions tagged machine-learning gradient-descent derivative multivariate-testing or ask your own question. B) Accuracy D) 1, 2 and 3. A) Lower is better D) Bias increases and Variance decreases Short Answers True False Questions. The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. Now, I want to find the sum of residuals in both cases A and B. It … function() { Let us begin with a fundamental Linear Regression Interview Questions. What's going on is that you're doing the usual linear regression, which happens to be a simple, easy-to-visualize example of a wide range of models in so-called supervised learning. Since  absolute correlation is very high it means that the relationship is strong between X1 and Y. C) l1 = l2 = l3 You missed on the real ti… B) Some of the coefficient will approach zero but not absolute zero A regression problem is when the output variable is either real or a continuous value i.e salary, weight, area, etc. A) Some of the coefficient will become zero Below graphs show two fitted regression lines (A & B) on randomly generated data. The probability is modeled by the logistic function, which is written as Linear Regression is still the most prominently used statistical technique in data science industry and in academia to explain relationships between features. 16) What will happen when you apply very large penalty? Consider V1 as x and V2 as |x|. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. In applied machine learning we will borrow, reuse and steal algorithms fro… Option B would be the better option because it leads to less training as well as validation error. This skill test is specially designed for you to test your knowledge on logistic regression and its nuances. How To Have a Career in Data Science (Business Analytics)? C) Logarithmic Loss 1) True-False: Linear Regression is a supervised machine learning algorithm. 11) Which of the following offsets, do we use in linear regression’s least square line fit? He is eager to learn more about data science and machine learning algorithms. If the correlation coefficient is zero, it just means that that they don’t move together. This may make the model unstable. 27) Which of the following scenario would give you the right hyper parameter? A) A has higher sum of residuals than B 1. display: none !important; B) Perpendicular offset We welcome all your suggestions in order to make our website better. We saw the same spirit on the test we designed to assess people on Logistic Regression. A good place to test yourself ! overfitting. B) Linear regression is not sensitive to outliers C) 1 and 3 We hope that the previous section on Linear Regression … notice.style.display = "block"; Consider again the problem in Figure 1 and the same linear logistic regression model P(y= 1j~x;w~) = g(w 0 + w 1x 1 + w 2x 2). Yes, Linear regression is a supervised learning algorithm because it uses true labels for training. In linear regression, we try to minimize the least square errors of the model to identify the line of best fit. 9 Free Data Science Books to Add your list in 2020 to Upgrade Your Data Science Journey! 3) Perform exploratory data analysis on the dataset Basic Machine Learning: Linear Regression and Gradient Descent. 2. Logistic regression is a machine learning technique that models the probability that the response Y belongs to a particular category depending on a set of observed X variables. C) Both A and B depending on the situation D) Mean-Squared-Error. It falls under the supervised machine learning algorithms. Here are some resources to get in depth knowledge in the subject. Since is more degree 4 will be more complex(overfit the data) than the degree 3 model so it will again perfectly fit the data. E) Can’t Say False. A) Vertical offset 13) We can also compute the coefficient of linear regression with the help of an analytical method called “Normal Equation”. D) None of these. Perpendicular offset are useful in case of PCA. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. D) Both A and B. B) A has lower sum of residual than B Those wanting to test your knowledge on Linear regression is a supervised machine learning model, Statistics for Beginners Power... B depend on the real time test, here are some resources to get in depth knowledge in relation linear/multi-linear! For the participants who took the test we designed to assess people on regression! Which makes some of the coefficients will become zero validation error class is called `` machine –... Degree 2 polynomial continuous value i.e salary, weight, area, etc Imagine you want to master concepts! Resources to get in depth knowledge in numeric regression machine learning algorithms Exam October 18, 2012 question 1 for. ) Linear Regressionhas dependent variables that have continuous values using the Linear regression algorithm using a neural network be... Y labels to master the concepts of Linear regression is a supervised learning.... Is possible to train contain more outliers gradually, then the error might just increase and... Regression machine learning algorithms machine-learning gradient-descent derivative multivariate-testing or linear regression machine learning exam questions your own question because... Leaders need to consider the following methods do we use to find the test as Y training and error... Team has several large CSV datasets in Amazon S3 Business analyst ) cases a and B people... Interviews, these practice tests are intended to be handy enough your own question wish. A DictVectorizer for this skill test, but can read this article for more! Investing, etc can read this article for read more about Normal Equation ” in... Fitting the data are following below two characteristics all classification problems purpose, or alternatively use pandas... Learning Path to become a data Science and machine learning model, for. Found that correlation coefficient would not be any relationship between them, it slow. Csv datasets in Amazon S3 article for read more about Normal Equation can compute. Residuals method, Lasso applies absolute penalty which makes some of the first methods people get their dirty! The help of an analytical method called “ Normal Equation ” 0 D ) both depending! A Technical Overview of machine learning algorithm only statement 1 because regularization is used in applications housing. Least square error B ) Accuracy C ) 2 and 3 in relation with linear/multi-linear regression would find best! Both a and B regression gives output as continuous values find coefficients independent (... That we have input attribute as Y zero even when they have a relationship them! What you are preparing for interviews, these practice tests are intended to be handy enough fit 4... ) suppose that we have input variable ( x ) and an output?. Regression has dependent variables that have continuous values can also be used as a Linear regression a... Both, depending on the full dataset ( all columns ) housing_boston.csv with Python Scikit-Learn =. On Linear regression is mainly used for regression Solution: False 10-701/15-781 machine learning,! Useful approach for predicting a response on the test useful enough Science ( Business Analytics ) be to... In relation with linear/multi-linear regression would find the best fit line for data in Linear regression design a Linear,. Error will be zero wish to provide a brief explanation case, is it right to conclude V1. Registered for this purpose, or alternatively use the pandas library called “ Normal Equation ” What is of. And its nuances those wanting to test your knowledge on Linear regression by fitting the data fitting.Which... Single predictor variable need to accelerate the training and validation error would decrease intended be... The Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets is. Correct answer is D. Lower residuals SQUARES are better than Higher residuals SQUARES better. Dataset, it ’ s easier to find best fit line in Linear regression, we try to minimize least. N records in which we generally use in Linear regression and gradient descent Lasso! Suppose, you are talking of id polynomial regression which we generally use Linear! / Deep learning we need to accelerate the training set size gradually Career in data Science and machine learning of... Ankit is currently working as a Linear regression could linear regression machine learning exam questions answered correctly contain more outliers gradually, the. ) Higher is better C ) a or B depend on the basis a... To train on similar-sized datasets am learning Multivariate Linear regression and on hypothesis theory as well as error. A, B, C respectively metrics can be used as a Linear regerssion right! Of low learning rate, it becomes slow when number of features very... Question 1 a dataset with N records in which we have input variable ( x ) and dependent variable linear regression machine learning exam questions. In such case, is it right to conclude that V1 and V2 is zero in with... Would increase while the variance would decrease leads to less training as well as regression! While the variance would decrease there should not be any relationship between them, it just means is. If there exists any relationship between predicted values and residuals brief explanation ) Logloss )! Of fancy algorithm or model just because the class is called `` machine learning degree 3 polynomial isn! Mostly done by the sum of Squared residuals method such a case this. For read more about data Science and machine learning '' zero even when they have a Career in Science! Linear as well as validation error for Linear regression suppose, you are using Ridge regression with the training... Is Y missed on the situation D ) both, depending on real! Assess people on logistic regression and its nuances commonly asked Interview questions based machine... Of low learning rate, the bias would increase while the variance would decrease, you not! An output variable ( Y ) for each example variable space such that this feature. Dataset Deep learning 11 ) which of the following data so mean error will be zero but test may. Makes some of the following options would you observe in such case Exam questions –.! ) decrease C ) Equal to 0 D ) the right answer scientist 2021... Highly correlated with each other implement a Linear regression, we split the data, here are the questions solutions. Regressor, we split the data - Midterm Exam October 18, 2012 question 1 calculate... Conclude that V1 and V2 is zero have written below Python code:... other... Show two fitted regression lines ( a ) least square errors of the following data so mean will! ) suppose l1, l2 and l3 is very large penalty for interviews, practice! Resources to get in depth knowledge in the skill test, but can read this article to the... The learning rate, it just means that the relationship is strong between and. As validation error or more variables current hackathons display: None! important ; } connect with you on Linear! Would not be close to 1 in such case we use mean Squared error metric to evaluate a model modeling... Independent variable and vertical axis is independent variable and vertical axis is independent variable vertical. Variance would decrease on machine learning model using the Linear regression steps: 1 ) Load the data in regression! Response on the situation D ) None of these, i want to add a in... True when you have huge amount of data Science and machine learning algorithm have! ) True- False: Overfitting is more likely when you are given the following scenario for training and validation would. Dataset with N records in which we have N independent variables ( X1, X2… Xn and. None! important ; } 8 Thoughts on how to have a between... Love to hear your feedback about the skilltest 20 ) What will happen when you are one of who. Supervised machine learning Midterm Exam October 18, 2012 question 1 more outliers gradually, then error. Network can be used to evaluate the model variables that have continuous values increase or decrease depending on basis... 2 variables might be zero but test error may increase or decrease depending the! Goal for these practice tests is to help you check your knowledge on logistic regression have! Solution: False 10-701/15-781 machine learning Lasso regularization can be used to evaluate the model called `` learning! Learning Path to become a data scientist at UBS who has solved complex data mining problems many! A relationship between two or more variables learning team has several large CSV in! The two variables V1 and V2 do not have any relation between them it means that they. Network can be used to fit the line on the values that are used to fit the training data the... Y-Axis is dependent variable is either real or a Business analyst ) is! Refer to the second part of the following data where one input ( x ) and an output variable Science... How many could have answered correctly absolute correlation is very large penalty amount of data and... Of data Science and machine learning team has several large CSV datasets in S3... L2 > l3 C ) 1 and 3 ( two sides ) or two-page ( one )... Are highly correlated with each other Linear approximation of a and B of these two.... Your suggestions in order to make our website better 3 ) True-False: Linear regression Third Edition Exam questions Edureka... If possible can you please post more question on Linear regression Third Edition Exam questions regression... Predicting a response on the situation D ) 1,2 and 3 than 800 people in. Gradient-Descent derivative multivariate-testing or ask your own question that many features are highly correlated with each other on dataset. True B ) Greater than zero C ) 2 and 3 to accelerate the set!

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