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?
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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?
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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?There are many factors when determining the performance of your model. What are some ways to evaluate regression versus classification 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 and why should you utilise the proportional, binomial, and poisson regression models? To what ends do these models use the degrees of flexibility they provide?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
- When it comes to doing an analysis of the data, what are the benefits of employing all-subsets regression rather than stepwise regression?What's the difference between logistic and linear regression? How do you avoid local minima in machine learning?When and why should you use a proportional, binomial, or poisson regression model? How do the models make use of adaptability?
- When do we utilize the proportional, binomial, and poisson regression models, and why do we employ them? How and why are these degrees of freedom included into each of these models?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) RegularizationIn 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?