An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
13th Edition
ISBN: 9781461471370
Author: Gareth James
Publisher: SPRINGER NATURE CUSTOMER SERVICE
expand_more
expand_more
format_list_bulleted
Expert Solution & Answer
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...
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
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?
In what ways are prescriptive models different from descriptive models?
In what ways are prescriptive models different from predictive models?
In what ways are descriptive models different from predictive models?
What is a dependent variable?
. What is an independent variable?
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 (Springer Texts in Statistics)
Knowledge Booster
Similar questions
- Q2arrow_forwardJustify 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_forwarda. 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_forward
- Is it feasible to differentiate between a prescriptive model and a descriptive model by analyzing the ways in which they vary from one another?arrow_forwardIn general, descriptive models are preferred over prescriptive models; yet, the question remains: which model is superior?arrow_forwardIn general, descriptive models are preferred over predictive ones, but which one is ideal?arrow_forward
- A model is used to generate a forecast using features as inputs and returning a prediction in a subset of machine learning called as is.... The following are some examples of new cutting-edge models that have become viable:arrow_forwardCan the distinction between a descriptive model and a prescriptive model be made by examining their differences?arrow_forwardThe subject is Engineering Data Analysisarrow_forward
- Justify 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_forwardAlthough descriptive models are favoured over prescriptive models on the whole, the question remains: which kind of model is superior?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_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Database System ConceptsComputer ScienceISBN:9780078022159Author:Abraham Silberschatz Professor, Henry F. Korth, S. SudarshanPublisher:McGraw-Hill EducationStarting Out with Python (4th Edition)Computer ScienceISBN:9780134444321Author:Tony GaddisPublisher:PEARSONDigital Fundamentals (11th Edition)Computer ScienceISBN:9780132737968Author:Thomas L. FloydPublisher:PEARSON
- C How to Program (8th Edition)Computer ScienceISBN:9780133976892Author:Paul J. Deitel, Harvey DeitelPublisher:PEARSONDatabase Systems: Design, Implementation, & Manag...Computer ScienceISBN:9781337627900Author:Carlos Coronel, Steven MorrisPublisher:Cengage LearningProgrammable Logic ControllersComputer ScienceISBN:9780073373843Author:Frank D. PetruzellaPublisher:McGraw-Hill Education
Database System Concepts
Computer Science
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:McGraw-Hill Education
Starting Out with Python (4th Edition)
Computer Science
ISBN:9780134444321
Author:Tony Gaddis
Publisher:PEARSON
Digital Fundamentals (11th Edition)
Computer Science
ISBN:9780132737968
Author:Thomas L. Floyd
Publisher:PEARSON
C How to Program (8th Edition)
Computer Science
ISBN:9780133976892
Author:Paul J. Deitel, Harvey Deitel
Publisher:PEARSON
Database Systems: Design, Implementation, & Manag...
Computer Science
ISBN:9781337627900
Author:Carlos Coronel, Steven Morris
Publisher:Cengage Learning
Programmable Logic Controllers
Computer Science
ISBN:9780073373843
Author:Frank D. Petruzella
Publisher:McGraw-Hill Education