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
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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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(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.
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?
Justify your preference for one machine learning model over another by providing illustrative examples. K-nearest neighbour analysis (a), historical data (b), and supplemental data (c, d) are two distinct methods of classifying items.
Chapter 2 Solutions
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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- Subject: Machine Learning Question Number 5 : What are Ensemble Algorithms? Write an algorithm for BOOSTING method. Assume that two individuals offer to sell you their predictive models M1 and M2. The confusion matrices produced by each model are as follows. What is the accuracy of each model? Assuming that precision is of paramount importance in your application, which of the two models would you buy? Why?arrow_forwardIs it feasible to have several dependant variables in a model? A choice dilemma may include more than one variable. When given the option between a descriptive, predictive, and prescriptive model of a given decision, which would you use? Why? Explain how a famous businessperson, politician, or military figure's good decision-making led to a disastrous outcome, or how the opposite was true. Fifth, how does a computer model vary from a spreadsheet model?arrow_forwardHow do you assess logistic regression versus simple linear regression models in machine learning concept ?arrow_forward
- Is it feasible to differentiate between descriptive and prescriptive models by looking at the differences between the two?arrow_forwardIs Logistic regression based on a linear combination of parameters as is decision tree?arrow_forwardThe following is true about sensitivity: Group of answer choices a) The output of the model is said to be inversely sensitive if the output of the model changes a small amount for a large change in an input variable b) Sensitivity is not an important concept in modeling c) It can help the modeler tell, on a relative basis, what are the important variables d) A variable is considered NOT very sensitive if a small change in the variable results `in a large change in the output of the model.arrow_forward
- Can the distinction between a descriptive model and a prescriptive model be made by examining their differences?arrow_forwardIt's AI and Machine Learning question Can You Explain The Bias-Variance Decomposition Of Classification Error In The Ensemble Method?with code. and . Explain The Term Perceptron In Machine Learning?arrow_forwardShould I always treat outliers before building my machine learning model or just leave the outlier in my dataarrow_forward
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