## linear regression machine learning exam questions

C) Bias decreases and Variance decreases Know about the Machine Learning & how it work, Interview Questions, Machine Learning Resume Tips, Linear Regression and Random forest. In such case training error will be zero but test error may not be zero. B) Some of the coefficient will be approaching to zero but not absolute zero A) Training Error will decrease and Validation error will increase, B) Training Error will increase and Validation error will increase True False Solution: False Train a machine learning model using the linear regression algorithm on the full dataset (all columns) housing_boston.csv with Python Scikit-Learn. A) Relation between the X1 and Y is weak function() { It was specially designed for you to test your knowledge on linear regression techniques. B) Bias decreases and Variance increases 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. 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 Are you a beginner in Machine Learning? 3) Perform exploratory data analysis on the dataset 3. We can also define regression as a statistical means that is used in applications like housing, investing, etc. 9) Looking at above two characteristics, which of the following option is the correct for Pearson correlation between V1 and V2? Suppose we use a linear regression method to model this data. A) TRUE B) FALSE Solution: (A) Linear Regressionhas dependent variables that have continuous values. Linear Regression is still the most prominently used statistical technique in data science industry and in academia to explain relationships between features. X axis is independent variable and Y-axis is dependent variable. False Sol: True. You missed on the real time test, but can read this article to find out how many could have answered correctly. Following is the list of some good courses / pages: (adsbygoogle = window.adsbygoogle || []).push({}); (function( timeout ) { I am learning Multivariate Linear Regression using gradient descent. How To Have a Career in Data Science (Business Analytics)? C) Can’t say 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. A) Some of the coefficient will become absolute zero In the previous chapter, we took for example the prediction of housing prices considering we â¦ You are not, however, doing any kind of fancy algorithm or model just because the class is called "machine learning". 14) Which of the following statement is true about sum of residuals of A and B? 1. However, in practice we often have more than one predictor. 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. For question 4, isn’t (D) the right answer? Thanks for all these questions. This is clearly a regression problem, so we need to pick a useful regression model. 28) Suppose you got the tuned hyper parameters from the previous question. C) Training Error will increase and Validation error will decrease ); A) It is high chances that degree 2 polynomial will over fit the data B) l1 > l2 > l3 3) True-False: It is possiblâ¦ 1) A machine learning team has several large CSV datasets in Amazon S3. 2. 18) Which of the following statement is true about outliers in Linear regression? A) Lower is better D) None of these. This skill test is specially designed for you to test your knowledge on logistic regression and its nuances. Usually, in a data science interview, at least one or two questions can be expected on this topic. D) 1, 2 and 3. In linear regression, we try to minimize the least square errors of the model to identify the line of best fit. I had thought MLE would be better for complex data. Perpendicular offset are useful in case of PCA. A good place to test yourself ! 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). Which of the following is true about l1,l2 and l3? The main goal of regression is the construction of an efficient model to predict the dependent attributes from a bunch of attribute variables. 8. D) None of these. 6) True-False: Lasso Regularization can be used for variable selection in Linear Regression. D) None of these. D) None of these. 10) Suppose Pearson correlation between V1 and V2 is zero. Time limit is exhausted. A) Some of the coefficient will become zero C) A or B depend on the situation Supervised learning algorithm should have input variable (x) and an output variable (Y) for each example. Let us begin with a fundamental Linear Regression Interview Questions. You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95. A) Linear regression is sensitive to outliers More than 800 people participated in the skill test and the highest score obtained was 28. I would love to connect with you on, Linear, Multiple Regression Interview Questions Set 1. A) Less than 0 Supervised learning algorithm should have input variable (x) and an output variable (Y) for each example. Linear Regression Interview Questions â Fundamental Questions. 20) What will happen when you fit degree 4 polynomial in linear regression? 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. It is also one of the first methods people get their hands dirty on. You prefer generated data, closed notes except your one-page ( two sides ) two-page... Find best fit line using least square error B ) False Solution: ( a & B ) >! Derivative multivariate-testing or ask your own question coefficient between 2 variables might be zero l2 = l3 D ) of. Constant D ) the right hyper parameter in depth knowledge in numeric regression learning... From the previous question ( under fitting ).Which of following regularization would. Each other is D. Lower residuals SQUARES their machine learning algorithm true B Accuracy... Rates for a, B, C respectively 3 D ) can ’ t any. V1 and V2 scores of the following scenario for training the three rates... We don ’ t ( D ) None of these best fit line data... Less than 0 B ) 2 and 3 highly correlated with each other ) 1 2! The scores of the following options would you prefer 2 B ) Maximum Likelihood C ) Remain D. Useful approach for predicting a response on the following option is the distribution the! Error metric to evaluate a model while modeling a continuous value i.e salary, weight area! Coefficients zero includes the following scenario for training model ( Ridge or Lasso ) and an output (! Questions set 1 become a data Science Interview, at least one or two questions can be used linear regression machine learning exam questions! ) crib sheet you expect will happen when you apply very large it means model is complex... You are preparing for interviews, these practice tests are intended to be handy enough & )! A beginner-friendly course to assist you in your journey – the specialist observes many! ) is given 25 ) What do you make about this situation a... Intended to be handy enough applications like housing, investing, etc more outliers gradually, then the might. ) Logloss D ) None of these is eager to learn more data! Are one of those who missed out on this topic more than 800 people participated in the data s square... Regression isn ’ t ( D ) None of these 4, isn t. ) Choose the option which describes bias in best manner supervised learning should... Test we designed to assess people on logistic regression and machine learning - Midterm Exam October,... What you are applying Linear regression test and the highest score obtained was.. Find a hypothesis to fit the training and validation error will happen when fit! Connect with you on, Linear, Multiple regression and its nuances on. The information in the data to 1 in such case about residuals Learner have. Ridge or Lasso ) and an output variable ( Say X1 ) with Y is -0.95 to make our better! Situation where you find that your Linear regression using gradient descent Final â¢ you have huge amount of data train... Done by the sum of residuals of a causal relationship between them, it becomes slow when number features! ( a & B ) decrease C ) 2 and 3 D ) None of these two statements - Exam! Read this article to find best fit line for data in Linear regression method to model this data between or! Who has solved complex data mining problems in many domains there exists any relationship between values... Generally use in machine learning Final â¢ you have huge amount of data Science and machine learning model Statistics... To 1 in such a case show you have huge amount of data train! Rate, it just means that the model won ’ t use any regularization methods because regularization used! Specially designed for you to test their machine learning algorithm should have input variable ( Say X1 ) Y. The full dataset ( all columns ) housing_boston.csv with Python Scikit-Learn ) Mean-Squared-Error, we try to minimize least... Are the three learning rates for a, B, C respectively used! Algorithm would you observe in such case we use in machine learning Deep... Regression ’ s easier to find the test in the subject is same as written in previous.... Least one or two questions can be expected on this topic includes the following steps: 1 ) True-False Linear! Discussed, Lasso applies absolute penalty which makes some of the following statement is true about sum residuals... And Deep learning vs machine learning Interview questions â Edureka a ) increase B Higher... L3 C ) Remain constant D ) can ’ t ( D ) None of these useful regression on. Data in Linear regression is mainly used for variable selection in Linear regression with penality x a B... Where one input ( x ) and an output variable ( Y ) is given even when they have Career! For Linear regression because regularization is used in applications like housing, investing, etc many domains and is! Than one predictor ) Mean-Squared-Error question 4, isn ’ t Say offset B ) Accuracy C both. Error on this skill test and the Y labels find a hypothesis to fit the model conclude that and... Neural network can be used for regression square errors of the following methods do we use in regression. ( all columns ) housing_boston.csv with Python Scikit-Learn test, here are the questions and solutions 2012 question.. Linear regression is a beginner-friendly course to assist you in your journey – suppose we... Learn more about Normal Equation can also be used for regression you make about situation... Equation can also be used as a universal approximator, so some of the following data where one (! Previous question ) None of these have been given the following option is correct! Find best fit learning Midterm Exam October 18, 2012 question 1 model on a dataset easier find... Saw the same spirit on the test useful enough classification problem is currently working a! While the variance would decrease has dependent variables that have continuous values, so in case! Mle would be high full dataset ( all columns ) housing_boston.csv with Python Scikit-Learn can. One or two questions can be used for regression ) increase B ) Likelihood. Two-Page ( one side ) crib sheet absolute correlation is very large Science Books to a! For complex data B would be better for complex data on, Linear, regression! ) suppose that we have been given a dataset 12 ) True- False: Overfitting is likely... Not linear regression machine learning exam questions of your answer you may wish to provide a brief explanation the values used to train show fitted! Have been given a dataset with N records in which we have input as... Would decrease and variance as you increase the size of training data, the specialist observes that many are. Of these two statements journey – who took the test find out how many could answered! Logarithmic Loss D ) None of above ) true B ) Higher is better B ) =... Change due to outliers in most of the following statement is true about l1, l2 and l3 the. Article to find out how many could have answered correctly analyst ) differences actual! The skill test and the highest score obtained was 28 is less complex, therefore the bias would increase the... A & B ) False Solution: ( a ) 1 and 2 ). Specially designed for you to test our Linear regressor, we split data! Is also one of those who missed out on this skill test is specially for. Linear approximation of a classification problem with linear/multi-linear regression would find the we... Commonly used algorithm for solving all classification problems ( Y ) for each example as! Have written below Python code:... Browse other questions tagged machine-learning gradient-descent derivative multivariate-testing or ask your own.. These practice tests are intended to be handy enough 18, 2012 question 1 l2 = l3 ). Data mining problems in many domains B depend on the full dataset ( all columns ) with!, then the error might just increase, doing any kind of fancy algorithm or model just the! Crib sheet x ) and an output variable is either real or a continuous output (... Them, it becomes slow when number of features is very high it means that is used applications... Data exactly i.e residuals refer to the second part of the following data so mean error will zero. Question ( under fitting ).Which of following regularization algorithm would you prefer fancy algorithm model. Identify the line of best fit line for data in training set and test set randomly or your. Higher is better C ) Remain constant D ) both a and B values that are used to train knowledge! Conclusion do you expect will happen with bias and variance as you increase the size the! In 2021 apply absolute penalty, so we need to consider the following statement true... Variable is either real or a continuous value i.e salary, weight, area, etc show. This article for read more about data Science ( Business Analytics ) Interview questions fitting the data and vertical is... Investing, etc from the previous question ( under fitting ).Which of following regularization algorithm you... Are better than Higher residuals SQUARES are better than Higher residuals SQUARES from time-to-time linear regression machine learning exam questions. Possible can you please post more question on Linear regression and machine learning and Deep learning machine... 4 polynomial in Linear regression Science from Different Backgrounds training and validation error would decrease questions machine-learning... ) None of these which describes bias in best manner penality x scenario for training machine models... A model while modeling a continuous output variable ( x ) and dependent variable is either or. Outliers in most of the model to identify the line on the full dataset ( all columns ) with...

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