You created a predictive model of a quantitative outcome variable using multiple regressions. What are the steps you would follow to validate the model?
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- How do you analyse the performance of the predictions generated by regression models versus classification models?When assessing the performance of your model, there are a lot of different elements to consider. How can we compare and contrast the predictive power of classification and regression models?How do you choose the best Linear Regression training strategy to utilise when you have a large training set with millions of features?
- When should the proportional, binomial, and poisson regression models be used, and what are the benefits? In what ways and for what purposes are the degrees of freedom incorporated in each of these models?In classification and regression trees (CART), it is done by the model itself, based on how dirty it is. Features that are used in CART are thought to be the most important parts of the tool. Some experts said that people should not have to choose features before they build CART. However, some other analysts disagreed and said that, as long as we need to run models, feature selection is still an important step before building a model. Before running CART models, do you think it is important for users to pick out the features they want to use?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
- What exactly is regression testing?What are the major differences in use-cases for linear regression as compared with non-linear types?How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything? Pick an algorithm you like and walk us through the math and then the implementation of it, in pseudo- code. Discuss how you go about feature engineering (look for both intuition and specific evaluation techniques). How can you build a simple logistic regression model in Python?