How can you handle overfitting in linear regression? (check all that apply; hint: choose two answers) ☐ use regularization (ridge or lasso) remove all categorical variables increase number of features use variable selection remove all outliers from the data
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A: - We need to have some of the applications of the linear regression.
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A: True
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- 7Question 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…Write a python programming code for logistic regression and calculate these classification metrics: contusion matrix, accuracy, precision, recall, sensitivity, specificity, F1 score and matthew correlation coefficient.
- i need help writting this codepandas python how to check for outliers for the culomn variable. subset the dataframe given z-score>3 or z-score <-3 and save it in dataframe named displacement_outliers. print the outliers dataframeThe difference between Linear Regression and Logistic Regression. Note: Please make in table and with your own word
- The R function for logistic regression is: Question 21 options: logit exp lm glmGradient Descent algorithmTask 4: Given the data set with two dimensions X and Y: Calculate every step and not using libraries X Y HE 1435 4232 Use a linear regression method to calculate the parameters a and ß where y = a + Bx. (Show every step and not using libraries)