Question 25 (1 point) Listen What happens when you overfit a regression model? The model captures too much of the random variation in the sample data and fails to predict accurately for the population. The p-value for the F-test of overall significance is greater than 0.05. So many variables are added to the model that the Adjusted R² value exceeds 1 None of the relationships in the model are found to be statistically significant.

Question 25 (1 point) Listen What happens when you overfit a regression model? The model captures too much of the random variation in the sample data and fails to predict accurately for the population. The p-value for the F-test of overall significance is greater than 0.05. So many variables are added to the model that the Adjusted R² value exceeds 1 None of the relationships in the model are found to be statistically significant.

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question 14 You do a study on the impact of listening to classical music in babies on IQ scores 5 years later, when entering elementary school. You enroll a large sample of randomly selected families in this study and the study consists of recording how long the baby listened to classical music and their later IQ score. Doing a simple linear regression, you find that you can strongly predict IQ scores from the hours listened to classical music. What should you do next? a) Publish a paper on the "Mozart effect" - how listening to classical music causes babies to be smarter. O b) Take this effect at face value, after all you did an experiment, and it is important to trust the science. c) Do a regression using socioeconomic status (SES) to predict hours listened to classical music. Then do an additional regression using SES to predict later IQ scores. Correlate the residuals of those two regressions and use that correlation in the paper you publish. d) Do an additional regression to see…
A study shows that the correlation between head circumference and IQ score is 0.50. If a least-squares regression was performed, which of the following would be true? (Select all that apply) 25% of the varability in IQ score is explained by head circumference O 50% of the varability in IQ score is explained by head circumference 25% of the varability in head circumference is explained by IQ score 50% of the varability in head circumference is explained by IQ score
Question 25 (1 point) Listen What happens when you overfit a regression model? The model captures too much of the random variation in the sample data and fails to predict accurately for the population. The p-value for the F-test of overall significance is greater than 0.05. So many variables are added to the model that the Adjusted R² value exceeds 1 None of the relationships in the model are found to be statistically significant.
The following figure depicts the decision boundary for the logistic regression model built to predict preference for light beer vs. regular beer (PREFERENCE = Light is the success or positive class). The explanatory variables are INCOME and AGE. INCOME has a positive coefficient and AGE has a negative coefficient. Predictions are made based on the depicted decision boundary. Four observations are circled. Match them with the appropriate descriptions. Age (in years) A B C D 100 80 60 20 0 -20 SE $10 $20 $30 Beer Preference $40 $50 $60 $70 Income (in US$ thousands) L.I.TJ False negative True positive True negative False positive $80 LA $90 Regular ● Light
5.) A training set has 1250 samples with a single feature x. You use the polynomial_features function defined below with max_degree=3 to augment the training set, and define a LinearRegression model as m = LinearRegression( fit_intercept=False ). You call the fit function on m using the augmented training set and corresponding labels. Which of the following correctly represents the model learned by Sci-kit Learn's LinearRegression? def polynomial_features(x, max_degree): return pd.DataFrame( { i: x ** i for i in range(max_degree+1) } ) m.coef_[0] + m.coef_[1]*x + m.coef_[2]*(x**2) + m.coef_[3]*(x**3) + m.coef_[4]*(: m.coef_[0]*x + m.coef_[1]*(x**2) + m.coef-[2]*(x**3) m.coef_[0] + m.coef_[1]*x + m.coef_[2]*(x**2) + m.coef_[3]*(x**3) m.coef_[0]*x + m.coef_[1]*(x**2) + m.coef_[3]*(x**3) + m.coef_[4]*(x**4)
You trained the regression model with 100 regressors and 1000 observations in the training and another 1000 in the test sample. You found that in-sample R2 over the training sample is 70% and the out-of-sample R2 over the test sample only - 30%. (select all that apply) a) Do you think there is any problem and how would you characterize it? Can adding more regressors (if you have them) help the model? b) Which approaches you may use to solve the problem? c) What would you expect the in-sample R2 to increase or decrease after that? What about the out-of-sample (test) R2?
You have trained a logistic regression classifier and planned to make predictions according to: Predict y=1 if ho(x) 2 threshold Predict y=0 if ho (x) < threshold For different threshold values, you get different values of precision (P) and recall (R). Which of the following is a reasonable way to pick the threshold value? O a Measure precision (P) and recall (R) on the test set and choose the value of P+R threshold which maximizes 2 Ob Measure precision (P) and recall (R) on the cross validation set and choose the P+R value of threshold which maximizes 2 Measure precision (P) and recall (R) on the cross validation set and choose the PR value of threshold which maximizes 2 P+R Measure precision (P) and recall (R) on the test set and choose the value of PR threshold which maximizes 2 P+R
1.) When and Why do we use Proportional model, Binomial model, and Poisson regression model? 2.) How and why are degrees od freedom infleunced by each of these models?
Why use LASSO shrinkage methods for linear regression? Select ALL that are correct. They allow for greater model interpretability. They increase the prediction accuracy of linear regression on training data. They slightly decrease bias and can substantially decrease the variance of the model. They slightly increase bias, but can substantially decrease the variance of the model.
A Ridge Linear Regression adds the sum of the squared values of the coefficients to the loss function to penalize large coefficients. Group of answer choices True False
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.