linear regression machine learning exam questions

22) In terms of bias and variance. Linear and Logistic regression are the most commonly used ML Algorithms. 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 See Unit 4.4.1. In linear regression, we try to minimize the least square errors of the model to identify the line of best fit. Explain the differences between Logistic and Linear regression? 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. C) Both A and B depending on the situation Option B would be the better option because it leads to less training as well as validation error. Therefore lower residuals are desired. D) None of these. B) Greater than zero A) Increase 1) True-False: Linear Regression is a supervised machine learning algorithm. 25) What do you expect will happen with bias and variance as you increase the size of training data? Know about the Machine Learning & how it work, Interview Questions, Machine Learning Resume Tips, Linear Regression and Random forest. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. 11) Which of the following offsets, do we use in linear regression’s least square line fit? 2 Multiple Linear Regression. Suppose, you got a situation where you find that your linear regression model is under fitting the data. If you are one of those who missed out on this skill test, here are the questions and solutions. 9) Looking at above two characteristics, which of the following option is the correct for Pearson correlation between V1 and V2? B) In case of very large x; bias is high (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. would look at person and predict if s/he has lack of Haemoglobin (red blood cells We have been given a dataset with n records in which we have input attribute as x and output attribute as y. E) None of the above. Refer this article for read more about normal equation. 3) True-False: It is possibl… Questions tagged [linear-regression] Ask Question For questions about linear regressions, an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables. If the values used to train contain more outliers gradually, then the error might just increase. Scale is same in both graphs for both axis. 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.”. D) Mean-Squared-Error. B) Some of the coefficient will approach zero but not absolute zero 27) Which of the following scenario would give you the right hyper parameter? The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. var notice = document.getElementById("cptch_time_limit_notice_14"); We can use a DictVectorizer for this purpose, or alternatively use the pandas library. C) Logloss Below is the distribution of the scores of the participants: You can access the scores here. B) A has lower sum of residual than B if ( notice ) A) There are high chances that degree 4 polynomial will over fit the data These 7 Signs Show you have Data Scientist Potential! More than 800 people took this test. If the correlation coefficient is zero, it just means that that they don’t move together. B) Relation between the X1 and Y is strong D) None of these. .hide-if-no-js { A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression. For question 4, isn’t (D) the right answer? C) Bias will be high, variance will be low Since a degree 2 polynomial will be less complex as compared to degree 3, the bias will be high and variance will be low. D) None of these. B) Decrease B) Bias will be low, variance will be high 10-601 Machine Learning Midterm Exam October 18, 2012 Question 1. D) None of above. Which of the following is true about below graphs(A,B, C left to right) between the cost function and Number of iterations? Get sample data 3. Residuals refer to the error values of the model. 9 Free Data Science Books to Add your list in 2020 to Upgrade Your Data Science Journey! Tutorial to data preparation for training machine learning model, Statistics for Beginners: Power of “Power Analysis”. 30) Now situation is same as written in previous question(under fitting).Which of following regularization algorithm would you prefer? Time: 80 minutes. 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. A) Some of the coefficient will become absolute zero What can a machine learning specialist do to address this concern? A total of 1,355 people registered for this skill test. 8) Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Start introducing polynomial degree variables. 21) What will happen when you fit degree 2 polynomial in linear regression? 29) In such situation which of the following options would you consider? D) None of these. We cannot comment on the correlation coefficient by using only statement 1. In the previous chapter, we took for example the prediction of housing prices considering we … C) Training Error will increase and Validation error will decrease B) Bias decreases and Variance increases In such case, is it right to conclude that V1 and V2 do not have any relation between them? This may make the model unstable. Remaining options are use in case of a classification problem. One of the favorite topics on which the interviewers ask questions is ‘Linear Regression.’ Here are some of the common Linear Regression Interview Questions that pop up in interviews all over the world. • The exam is closed book, closed notes except your one-page (two sides) or two-page (one side) crib sheet. If V1 increases then V2 also increases, 2. C) A or B depend on the situation C) Both, depending on the situation A) In case of very large x; bias is low Logistic Regression is likely the most commonly used algorithm for solving all classification problems. A linear regression is a linear approximation of a causal relationship between two or more variables. C) Can’t say • Please use non-programmable calculators only. 5) Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable? A Review of 2020 and Trends in 2021 – A Technical Overview of Machine Learning and Deep Learning! However, in practice we often have more than one predictor. C) Both have same sum of residuals 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. 1. I would love to connect with you on, Linear, Multiple Regression Interview Questions Set 1. C) Both A and B depending on the situation D) None of these. 4) Which of the following methods do we use to find the best fit line for data in Linear Regression? Since  absolute correlation is very high it means that the relationship is strong between X1 and Y. If you are not sure of your answer you may wish to provide a brief explanation. True. 12) True- False: Overfitting is more likely when you have huge amount of data to train? seven D) 1, 2 and 3. Applied Machine Learning – Beginner to Professional, Natural Language Processing (NLP) Using Python, 45 Questions to test a data scientist on basics of Deep Learning (along with solution), 40 Questions to test a Data Scientist on Clustering Techniques (Skill test Solution). With a small training dataset, it’s easier to find a hypothesis to fit the training data exactly i.e. Time limit is exhausted. If you are given the two variables V1 and V2 and they are following below two characteristics. 2) Preprocess the dataset. Machine Learning Final • You have 3 hours for the exam. A) TRUE B) FALSE Solution: (A) Linear Regressionhas dependent variables that have continuous values. 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. 3) Perform exploratory data analysis on the dataset C) l1 = l2 = l3 A) It is high chances that degree 2 polynomial will over fit the data 3. A) Lower is better Linear, Multiple Regression Interview Questions Set 2, Linear, Multiple Regression Interview Questions Set 3, Linear, Multiple Regression Interview Questions Set 4, Bias & Variance Concepts & Interview Questions, Machine Learning Free Course at Univ Wisconsin Madison, Overfitting & Underfitting Concepts & Interview Questions, Uber Machine Learning Interview Questions, Reinforcement Learning Real-world examples, Starting on Analytics Journey – Things to Keep in Mind, Concepts related with simple linear regression and multi-linear regression, Tests such as T-test, ANOVA tests for hypothesis testing. Do you want to master the concepts of Linear Regression and Machine Learning? As already discussed, lasso applies absolute penalty, so some of the coefficients will become zero. Here is a beginner-friendly course to assist you in your journey –. machine learning quiz and MCQ questions with answers, data scientists interview, question and answers in bayesian net, support vectors, binary classifier, linear regression in machine learning, top 5 questions B) 1 and 3 Instead of gradient descent, Normal Equation can also be used to find coefficients. A good place to test yourself ! D) None of these. The absolute value of the correlation coefficient denotes the strength of the relationship. 1. 18) Which of the following statement is true about outliers in Linear 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. 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).  −  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 Thanks for all these questions. I won’t use any regularization methods because regularization is used in case of overfitting. function() { A Neural network can be used as a universal approximator, so it can definitely implement a linear regression algorithm. I am learning Multivariate Linear Regression using gradient descent. D) None of these. C) Bias decreases and Variance decreases You missed on the real ti… So the objective function will decrease slowly. D) 1,2 and 3. It … I would love to hear your feedback about the skilltest. A regression problem is when the output variable is either real or a continuous value i.e salary, weight, area, etc. Machine Learning Final • Please do not open the exam before you are instructed to do so. If possible can you please post more question on Linear as well as Multiple regression and on Hypothesis theory as well. 6) True-False: Lasso Regularization can be used for variable selection in Linear Regression. Welcome to the second part of the series of commonly asked interview questions based on machine learning algorithms. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression … Suppose we use a linear regression method to model this data. 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). It is used to predict the relationship between a dependent variable and a … • Mark your answers ON THE EXAM ITSELF. You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. He is eager to learn more about data science and machine learning algorithms. The slope of the regression line will change due to outliers in most of the cases. 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) AUC-ROC 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. Here is the leaderboard for the participants who took the test. D) Training Error will decrease and Validation error will decrease It falls under the supervised machine learning algorithms. True False Solution: False B) Since the there is a relationship means our model is good C) Remain constant B) Higher is better 16) What will happen when you apply very large penalty? If you are one of those who missed out on this skill test, here are the questions and solutions. C) Can’t say Yes, Linear regression is a supervised learning algorithm because it uses true labels for training. I have been recently working in the area of Data Science and Machine Learning / Deep Learning. Train a machine learning model using the linear regression algorithm on the full dataset (all columns) housing_boston.csv with Python Scikit-Learn. Let us begin with a fundamental Linear Regression Interview Questions. More than 800 people participated in the skill test and the highest score obtained was 28. The probability is modeled by the logistic function, which is written as Basic Machine Learning: Linear Regression and Gradient Descent. Which of the following is/are true about Normal Equation? Are you a beginner in Machine Learning? 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. ); I have written below python code: ... Browse other questions tagged machine-learning gradient-descent derivative multivariate-testing or ask your own question. You are not, however, doing any kind of fancy algorithm or model just because the class is called "machine learning". In lasso some of the coefficient value become zero, but in case of Ridge, the coefficients become close to zero but not zero. Now, I want to find the sum of residuals in both cases A and B. 1) True-False: Linear Regression is a supervised machine learning algorithm. Please feel free to share your thoughts. 1) A machine learning team has several large CSV datasets in Amazon S3. Design a model that works onthat sa… A) 1 and 2 B) Higher is better C) Logarithmic Loss In applied machine learning we will borrow, reuse and steal algorithms fro… Be sure to write your name and Penn student ID (the 8 bigger digits on your ID card) on the answer form and ll in the associated bubbles in pencil. 15) Choose the option which describes bias in best manner. CIS 520: Machine Learning Midterm, 2016 Exam policy: This exam allows one one-page, two-sided cheat sheet; No other materials. C) Relation between the X1 and Y is neutral D) Bias increases and Variance decreases True b. A) Since the there is a relationship means our model is not good If the added feature is important, the training and validation error would decrease. D) Both A and B. Now, Imagine you want to add a variable in variable space such that this added feature is important. 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. Q4. 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. 2. 2) True-False: Linear Regression is mainly used for Regression. We saw the same spirit on the test we designed to assess people on Logistic Regression. I had thought MLE would be better for complex data. D) None of these. The main goal of regression is the construction of an efficient model to predict the dependent attributes from a bunch of attribute variables. C) Pearson correlation will be close to 0 You missed on the real time test, but can read this article to find out how many could have answered correctly. 13) We can also compute the coefficient of linear regression with the help of an analytical method called “Normal Equation”. Explain Classification and Regression. Supervised learning algorithm should have input variable (x) and an output variable (Y) for each example. A) 1 and 2 Machine Learning: Supervised - Linear Regression. C) Can’t say 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. We can also define regression as a statistical means that is used in applications like housing, investing, etc. A) Relation between the X1 and Y is weak 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)? Here are some resources to get in depth knowledge in the subject. This skill test is specially designed for you to test your knowledge on logistic regression and its nuances. }, 24) Now we increase the training set size gradually. Solutions for Applied Linear Regression Third Edition Exam Questions – Regression. timeout B) Maximum Likelihood If the penalty is very large it means model is less complex, therefore the bias would be high. 10-701/15-781 Machine Learning - Midterm Exam, Fall 2010 Aarti Singh Carnegie Mellon University 1. Simple linear regression is a useful approach for predicting a response on the basis of a single predictor variable. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. 7) Which of the following is true about Residuals ? D) Can’t Say. B) Some of the coefficient will be approaching to zero but not absolute zero Consider the following data where one input(X) and one output(Y) is given. We welcome all your suggestions in order to make our website better. D) Bias will be low, variance will be low. A) Bias increases and Variance increases Perpendicular offset are useful in case of PCA. C) Can’t say 2) True-False: Linear Regression is mainly used for Regression. Should I become a data scientist (or a business analyst)? Consider V1 as x and V2 as |x|. C) A or B depend on the situation 19) Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. A) A has higher sum of residuals than B Can’t we use OLS or MLE to find best fit line in Linear Regression? If there exists any relationship between them,it means that the model has not perfectly captured the information in the data. A) Less than 0 What is linear regression? 3) True-False: It is possible to design a Linear regression algorithm using a neural network? 23) Suppose l1, l2 and l3 are the three learning rates for A,B,C respectively. Short Answers True False Questions. 8 Thoughts on How to Transition into Data Science from Different Backgrounds. To test our linear regressor, we split the data in training set and test set randomly. 1. Time limit is exhausted. Classification vs Regression – Machine Learning Interview Questions – Edureka B) Linear regression is not sensitive to outliers Training error may increase or decrease depending on the values that are used to fit the model. 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. In such case training error will be zero but test error may not be zero. 14) Which of the following statement is true about sum of residuals of A and B? A) Least Square Error As we increase the size of the training data, the bias would increase while the variance would decrease. A) Some of the coefficient will become zero Pearson correlation coefficient between 2 variables might be zero even when they have a relationship between them. Please reload the CAPTCHA. a machine learning approach. C) 2 and 3 The correlation coefficient would not be close to 1 in such a case. B) Accuracy 8. C) Equal to 0 })(120000); B) 2 and 3 5 Questions which can teach you Multiple Regression (with R and Python), Going Deeper into Regression Analysis with Assumptions, Plots & Solutions. 3. 2. It is mostly done by the Sum of Squared Residuals Method. 3. True, In case of lasso regression we apply absolute penalty which makes some of the coefficients zero. Supervised learning algorithm should have input variable (x) and an output variable (Y) for each example. Since linear regression gives output as continuous values, so in such case we use mean squared error metric to evaluate the model performance. A) Lower is better If V1 decreases then V2 behavior is unknown, A) Pearson correlation will be close to 1 }. Includes the following steps: 1) Load the data. Ankit is currently working as a data scientist at UBS who has solved complex data mining problems in many domains. 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. The team’s leaders need to accelerate the training process. We can perfectly fit the line on the following data so mean error will be zero. D) None of these. State the assumptions in a linear regression model. Which of the following conclusion do you make about this situation? There should not be any relationship between predicted values and residuals. Maybe try out some linear model (Ridge or Lasso) and compare it to a more complex model? B) There are high chances that degree 4 polynomial will under fit the data Following is the list of some good courses / pages: (adsbygoogle = window.adsbygoogle || []).push({}); (function( timeout ) { What you are talking of id Polynomial Regression which we generally use in Machine Learning. Thank you for visiting our site today. Thanks for making it possible to train our knowledge regarding regression techniques. D) Correlation can’t judge the relationship. False Sol: True. C) We can’t say about bias A) Bias will be high, variance will be high 7 Types of Regression Techniques you should know! We calculate the direct differences between actual value and the Y labels. Please reload the CAPTCHA. This page lists down the practice tests / interview questions and answers for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. This is clearly a regression problem, so we need to pick a useful regression model. For more such skilltests, check out our current hackathons. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. We don’t have to choose the learning rate, It becomes slow when number of features is very large. So Linear Regression is sensitive to outliers. 10) Suppose Pearson correlation between V1 and V2 is zero. B) l1 > l2 > l3 We hope that the previous section on Linear Regression … D) None of these. A) TRUE B) FALSE Solution: (A) Yes, Linear regression is a supervised learning algorithm because it uses true labels for training. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. Which of the following thing would you observe in such case? D) None of these, Sum of residuals will always be zero, therefore both have same sum of residuals. Great effort! But one question, a degree 3 polynomial regression isn’t considered as a linear regerssion model right?  =  zero (adsbygoogle = window.adsbygoogle || []).push({}); This article is quite old and you might not get a prompt response from the author. Below graphs show two fitted regression lines (A & B) on randomly generated data. It was specially designed for you to test your knowledge on linear regression techniques. 20) What will happen when you fit degree 4 polynomial in linear regression? A Comprehensive Learning Path to Become a Data Scientist in 2021! setTimeout( Which of the following is true when you fit degree 2 polynomial? This page lists down the practice tests / interview questions for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. Which of the following is true about l1,l2 and l3? We need to consider the both of these two statements. B) It is high chances that degree 2 polynomial will under fit the data What is logistic regression? Really helped. B) Pearson correlation will be close to -1 Suppose you have been given the following scenario for training and validation error for Linear Regression. Linear Regression is still the most prominently used statistical technique in data science industry and in academia to explain relationships between features. Linear Regression has dependent variables that have continuous values. We first convert the spreadsheet into a matrix. How To Have a Career in Data Science (Business Analytics)? Suppose horizontal axis is independent variable and vertical axis is dependent variable. Before we dive into the details of linear regression, you may be asking yourself why we are looking at this algorithm.Isn’t it a technique from statistics?Machine learning, more specifically the field of predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability. It is also one of the first methods people get their hands dirty on. D) None of these. E) Can’t Say False. In case of low learning rate, the step will be small. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. A) Vertical offset Deep Learning vs Machine Learning – Machine Learning Interview Questions – Edureka. As the training set size increases, what do you expect will happen with the mean training error? 17) What will happen when you apply very large penalty in case of Lasso? We always consider residuals as vertical offsets. display: none !important; Linear Regression Interview Questions – Fundamental Questions. D) None of these. notice.style.display = "block"; 1. A) Linear regression is sensitive to outliers I tried my best to make the solutions as comprehensive as possible but if you have any questions / doubts please drop in your comments below. We can take examples like y=|x| or y=x^2. 28) Suppose you got the tuned hyper parameters from the previous question.

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