Home » Data Science » Machine Learning » 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? Q: Practice More Questions From: Disease Detection With Computer Vision 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 Which of the following are valid methods for determining ground truth? Choose all that apply. Let’s say you have a relatively small training set (~5 thousand images). Which training strategy makes the most sense? Now let’s say you have a very large dataset (~1 million images). Which training strategies will make the most sense? Which of the following is not one of the key challenges for AI diagnostic algorithms that is discussed in the lecture? What is the total loss from the normal (non-mass) examples in this example dataset? In what order should the training, validation, and test sets be sampled? 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? What is the typical size of medical image dataset? Which of the following data augmentations would be best to apply? Why is it bad to have the same patients in both training and test sets? Created with Fabric.js 4.6.0 Practice More Questions Data Analysis 200+ Qs Machine Learning 100+ Qs