Home » Data Science » Machine Learning » You have a model such that the lowest score for a positive example is higher than the maximum score for a negative example. What is its ROC AUC? Q: Practice More Questions From: Evaluating Machine Learning Models Created with Fabric.js 4.6.0 Similar Questions You are working with the penguins dataset. You want to use the summarize() and max() functions to find the maximum value for the variable flipper_length_mm. You write the following code:penguins…You want to use the summarize() and max() functions to find the maximum rating for your data. Add the code chunk that lets you find the maximum value for the variable Rating. What is the maximum…You’ve fit a random forest of 10 trees with max depth 20. Your training ROC is 0.99 and test ROC is 0.54. Which of the following is NOT a reasonable thing to try?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?In some studies, you may have to compute the Positive predictive value (PPV) from the sensitivity, specificity and prevalence.Model 1 has a c-index of 0.7 and Model 2 has a c-index of 0.6. Which is more accurate using a threshold of 0.5 for the risk score?What is the sensitivity and specificity of a pneumonia model that always outputs positive? In other words, the models says that every patient has the disease.Look at the output of model 1 and model 2: Which one will have a lower soft dice loss?What is the sensitivity and specificity of a model which randomly assigns a score between 0 and 1 to each example (with equal probability) if we use a threshold of 0.7?We have the following table the output of a model f on an example using subsets of the variable. What is the Shapley value for s_BP?We have the following table the output of a model f on an example using subsets of the variable. What is the sum of the Shapley value for s_BP and d_BP? True or False: A tree of depth 1 is more expressive than a classical linear model.You have created a model using mean imputation. At test time, you should fill in missing values with:A data analyst uses the bias() function to compare the actual outcome with the predicted outcome to determine if the model is biased. They get a score of 0.8. What does this mean?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… Created with Fabric.js 4.6.0 Practice More Questions Data Analysis 200+ Qs Machine Learning 100+ Qs