An introduction to statistical learning: with applications in R
13th Edition
ISBN: 9781461471387
Author: James, Gareth, Witten, Daniela, Hastie, Trevor, TIBSHIRANI, Robert
Publisher: MPS (CC)
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Chapter 2, Problem 5E
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Advantages and disadvantages of flexible approach
- Less flexible and inference is more flexible when a model may not be a perfect fit.
- It fits well for linear models...
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An introduction to statistical learning: with applications in R
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- 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?arrow_forwardWhat exactly is biased evaluation, and what does decoupling mean in order to avoid biased evaluation? Explain your answer with an example of your own.arrow_forwardThere are many factors when determining the performance of your model. What are some ways to evaluate regression versus classification models?arrow_forward
- Write the objectives of regression testing and what are the situations to perform regression testing?arrow_forwardAre you aware that one kind of regression analysis is called Summing Squares Regression, or SSR for short?arrow_forwardWhat is the difference between discrimination and classification? Between characterization and clustering? Between classification and regression? For each of these pairs of tasks, how are they similar?arrow_forward
- What is an advantage of anomaly detection formulation over classification formulation? A. Both of the formulations are the same. B. There is no need to have label of "anomaly" which could be hard to find by their definition. C. There are many more well-studied techniques to use than in classification techniques. D. The evaluation is much simpler than in the classification setting.arrow_forwardIn 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?arrow_forwardDefine sum of squares regression (SSR)?arrow_forward
- What is a stepwise regression?arrow_forwardWhat's the difference between logistic and linear regression? How do you avoid local minima in machine learning?arrow_forwardIn classification and regression trees (CART), it is performed by the model itself based on impurity measures. Features that are used in CART are considered the most important ones. Some analysts suggested that users do not have to select features prior to the construction of CART. However, some other analysts disagreed and argued that, as long as we need to run models, feature selection remains a critical step before model construction. Do you agree that feature selection by users before running CART models is important?arrow_forward
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