Home » Data Science » Machine Learning » Let’s say blood pressure (BP) measurements are more likely to be missing among young people, who generally have lower blood pressure. You use mean imputation to train your model. Which option correctly characterizes the mean BP (after imputation) in your training dataset? Q: Practice More Questions From: Prognosis With Tree-Based Models Created with Fabric.js 4.6.0 Practice More Questions Data Analysis 2000+ Qs Machine Learning 1000+ Qs Created with Fabric.js 4.6.0 Similar Questions A linear risk model for the risk of heart attack has three inputs: Age, Systolic Blood Pressure (BP), and the interaction term between Age and Systolic Blood Pressure. The coefficients for…You want to measure the proportion of people with high blood pressure in a population. You sample 1000 people and find that 55% have high blood pressure with a 90% confidence interval of (50%,…Let’s say you have a relatively small training set (~5 thousand images). Which training strategy makes the most sense? You have created a model using mean imputation. At test time, you should fill in missing values with:Now let’s say you have a very large dataset (~1 million images). Which training strategies will make the most sense?Let’s see why F1 is used instead of the regular mean of precision and recall. Let’s say the mean of precision and recall is at least 0.75. Which of the following could be the true value of the…You are working with the penguins dataset. You want to use the summarize() and mean() functions to find the mean value for the variable body_mass_g. You write the following code:penguins…You train the random forest pictured below and it gets a c-index of 0.90. After shuffling the values for x, your dataset is the following. What is the variable importance for x?True or False: When your data is missing at random, then whether or not you are missing a covariate is completely independent of your outcome.Look at the output of model 1 and model 2: Which one will have a lower soft dice loss?Assume you have missing data on one of your features, and are considering two options: True or False: "Both options have the same performance".You find that your training set has 70% negative examples and 30% positive. Which of the following techniques will NOT help for training this imbalanced dataset?You want to use the summarize() and mean() functions to find the mean rating for your data. Add the code chunk that lets you find the mean value for the variable Rating. What is the mean rating?Now let’s say F1 score is at least 0.75. Now which of the following values of precision are possible? Using the S-Learner, or Single Tree, method, what is the conditional average treatment effect for a 61 year-old patient with a blood pressure (BP) of 140? Created with Fabric.js 4.6.0 Practice More Questions Data Analysis 200+ Qs Machine Learning 100+ Qs