How do you analyse the performance of the predictions generated by regression models versus classification models?
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How do you analyse the performance of the predictions generated by regression models versus classification models?
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- What are the major differences in use-cases for linear regression as compared with non-linear types?What are some creative ways to use binary variables in model formulation?How do you choose the best Linear Regression training strategy to utilise when you have a large training set with millions of features?
- What is the type of method to create regression models in which the coefficients are penalized for being too large than what they should be if multicollinearity was not there? Answer Choices: a) Elastic Net b) Lasso c) Ridge d) RegularizationThe output for linear regression analysis has multiple numbers. How can we interpret the output? Can you share some hints.a. Feature selection can be done through both filter and wrapper method. Which of these methods is more accurate and which of these is more efficient? Explain the tradeoff and justify your answer with an example. b. Why in some situations logistic regression model is preferred over linear regression?
- Write the objectives of regression testing and what are the situations to perform regression testing?Write the objective function that can be used to determine the regression model parameters. How is this objective function will be used to find model parameters?What is the name given to an issue which arises in multiple regression when there is high correlation among two or more independent variables? Answer Choices: a) Heteroscedasticity b) Multicollinearity c) Autocorrelation d) Serial correlation