COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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What is True (You may have multiple answer)
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Training accuracy = 0.90, Testing accuracy = 0.89, your model is good enough to deploy for diabetes prediction
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Area under curve of ROC is 0.9, your model is good enough to deploy for lung cancer prediction
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Training accuracy = 0.90, Testing accuracy = 0.89, your model is not overfit
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Area under curve of PR Curve is 0.9, your model is good enough to deploy for lung cancer prediction
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Explanation:
Detailed explanation-1: -Accuracy, confusion matrix, log-loss, and AUC-ROC are some of the most popular metrics. Precision-recall is a widely used metrics for classification problems.
Detailed explanation-2: -What are typical sizes for the training and test sets? 60% in the training set, 40% in the testing set.
Detailed explanation-3: -Explanation : The most common issue when using Machine Learning is a Poor Data Quality.
Detailed explanation-4: -Accuracy is one metric for evaluating classification models. Informally, accuracy is the fraction of predictions our model got right. Formally, accuracy has the following definition: Accuracy = Number of correct predictions Total number of predictions.
There is 1 question to complete.