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 4E
a.
Explanation of Solution
Classification or regression problem
- Classification is the problem of predicting a discrete class label and regression is the problem of predicting a continuous quantity...
b.
Explanation of Solution
Classification or regression problem
- Classification is the problem of predicting a discrete class label and regression is the problem of predicting a continuous quantity...
c.
Explanation of Solution
Classification or regression problem
- Classification is the problem of predicting a discrete class label and regression is the problem of predicting a continuous quantity...
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What is the primary objective of linear regression in machine learning?
A) Maximizing accuracy of predictions.B) Minimizing the number of features.C) Minimizing the sum of squared differences between predicted and actual values.
D) Maximizing the correlation coefficient between variables.
A) What is regression?
B) How we can use it to estimate relationship between dependent and independent variables?
C) How we can use it in machine learning?
Is it feasible to distinguish between a prescriptive and a descriptive model by comparing their differences?
Chapter 2 Solutions
An introduction to statistical learning: with applications in R
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Similar questions
- Justify your preferred machine learning model's use in a given scenario. There are two primary ways to classify items: (A) reminiscing, (C) using K-nearest neighbour, and (D) gaining insight.arrow_forwardC.W. Churchman once said that "Mathematics...tends to lull the unsuspecting that he who thinks elaborately thinks well." Do you think that the best QA Models are the ones that are most elaborate and complex Mathematically? Why?arrow_forward(a) What is a Loss function in Machine Learning? (b) Discuss at least two Loss functions from Regression type and at least two Loss functions from Classification type.arrow_forward
- Could you perhaps supply some examples to support your choice of machine learning model?arrow_forwardIn the following, we will discuss the most important characteristics of descriptive models?arrow_forwardIn general, descriptive models are preferred over prescriptive models; yet, the question remains: which model is superior?arrow_forward
- Can the distinction between a descriptive model and a prescriptive model be made by examining their differences?arrow_forwardIn this section, you will find four distinct machine learning algorithms that may be utilized for supervised learning on a dataset that has been supplied to you. Provide an explanation of any four factors you would use to assist in determining which one you would use to do the job of determining if a tumor is malignant or not??arrow_forwardJustify your choice of a particular machine learning model and why you believe it would be beneficial in your chosen setting. There are primarily two ways to classify things: (A) by memorising them, (C) by applying K-nearest neighbour algorithms, and (D) by gaining insight.arrow_forward
- 2. A. Explain excessive learning-overfitting in classification, missing learning-under fitting problems, and how these problems can be solved.B. As shown in the following figure, explain Bias, Variance, Complexity concepts, and relationships with each other in the classification.arrow_forwardDo the distinctions between the two make it possible to distinguish between descriptive and prescriptive models?arrow_forwardFor learning a predictive model describe the coefficient of determination and explicate its usefulness in Linear Regressionarrow_forward
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