INTRODUCTION TO STATISTICAL LEARNING
21st Edition
ISBN: 9781071614174
Author: James
Publisher: SPRINGER
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Chapter 2, Problem 2E
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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In what situations would you utilize the Proportional, Binomial, or Poisson regression models, and why? Degrees of freedom are included in each of these models, but how and why exactly do they do so?
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?
How do you analyse the performance of the predictions generated by regression models versus classification models?
Chapter 2 Solutions
INTRODUCTION TO STATISTICAL LEARNING
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- When are the proportional, binomial, and poisson regression models used, and why are they utilized?How and why are these degrees of freedom included into every model?arrow_forwardHow and when should we use the Proportional, Binomial, and Poisson regression models? Please explain how and why degrees of freedom are included into each of these models.arrow_forwardUse proportional, binomial, and poisson regression models when and why? How do these models employ flexibility?arrow_forward
- Draw logistic regression flow chart First design the question (choose x1 and x2 values for each class (blue and red) or initialize weight and bias),then find proper weight and bias values of green line that can be seen in Figure 1. This line should separate two classes.arrow_forwardProportional, binomial, and Poisson regression models are employed in various contexts within academic research. The selection of a specific regression model depends on the nature of the data and the research question at hand. Proportional regression models are commonly used when the outcome variable is continuous and the relationship between the predictor variables and the outcome is assumed to be How do these models utilise flexibility in their implementation?arrow_forwardWhat 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 correlationarrow_forward
- When and why do we utilize Poisson, Proportional, and Binomial regression models? How and why are degrees of freedom included in each of these models?arrow_forwardThe difference between Linear Regression and Logistic Regression. Note: Please make in table and with your own wordarrow_forwardQuestion # 1: Consider the following data of Regression Model where YACTUAL is your actual observation and YPREDICTION is the model prediction value. You have to use the data and Compute the value of the following errors: Mean Absolute Error, Mean Relative Error and Prediction (X) when X ≥ 30% Value of R2 YACTUAL 12.6 9.8 9.6 9.9 11.5 11.2 12.3 9.5 9.7 12.4 YPREDICTED 12.8 9.1 9.7 9.9 11.3 12.1 12.0 9.8 9.6 12.2arrow_forward
- When and why are proportional, binomial, and Poisson regression models used?arrow_forwardCreate a visual representation that demonstrates the concept of slope in linear regression and explain the key components and their interrelationships.arrow_forwardWhen 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?arrow_forward
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