What does it mean when the VIF (Variance Inflation Factor) of a variable is high in linear regression model? Answer Choices: a) The information explained through that variable is not being explained by other X variables given in that model. b) The information explained through that variable is already being explained by other X variables in that model. c) The model is useless. d) None of the above. Answer D

What does it mean when the VIF (Variance Inflation Factor) of a variable is high in linear regression model? Answer Choices: a) The information explained through that variable is not being explained by other X variables given in that model. b) The information explained through that variable is already being explained by other X variables in that model. c) The model is useless. d) None of the above. Answer D

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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The 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.
Now suppose that you have two versions of your model with different parameters(e.g., different regularization) or even different model families (e.g., logistic regression versus random forest). Which one is better?
Stationarity of a Moving Average Model automatically follows from stationarity of the error terms. Claim that whether it is true or false and explain it.
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?
How can you recognize whether a model satisfies proportionality andadditivity?
The data normalization process is one of the pre-processing stages needed to improve model performance. Even so, some machine learning models use calculations that pay attention to the unit of predictor variables used so that the normalization process is something that must be done. From the models under which model requires data normalization process? a) Multiple Linear Regression] b) Random Forest c) Binomial Logistic Regression d) k-NN
If we add more independent variables into the model: A. The adjusted R2 value will increase. B. The R2 value will increase. C. The R2 value will decrease if the variables we are adding into the model should not be there. D. The R2 will be biased.
What can surrogate models enable? Check all that apply. A) They can help linearize strongly nonlinear forward models. B) They can help accelerate evaluation of the forward model or likelihood in Bayesian inference. C) They can make it easier to exploit parallel computing resources. D) They can reduce the dimensionality of the data. E) They can help push more computation to an "offline" phase of the analysis.
PCA tried to find new basis vectors (axes) that maximize the variance of the instances. Is True or False?
Proportional, 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?
e) What are the methods to convert a categorical output data for using an Artificial Neural Network (ANN)? How would you use each method to convert the categorical attribute income range with possible values 10–20K, 20–30K, 30–40K, 40–50K? Which method is most appropriate? f) Explain why the updating term for the current weight in an ANN includes the negative of the sign of the derivative (slope).
4 the task is to estimate two models1. Cobduglus (after taking log to convert it into log-linear)2. Estimate the linear model without log