1. A machine learning model is designed to predict whether a given email is spam or not. The model has an accuracy of 95%, a false positive rate of 2% and a false negative rate of 5%. If 10% of all emails received are spam, what is the probability that an email identified as spam the model is actually spam? 2. A company has a screening test for potential employees that has a false positive rate of 4% and a false negative rate of 2%. if 10% of the applicants are actually qualified for the job, what is the probability that a person who tests negative is actually qualified for the job? 3. A machine learning model is trained to predict whether a given image contains a certain object or not. The model has an accuracy of 90% a false positive rate of 5%, and false negative rate of 8%. if 20% of the images in the dataset contain the object, what is the probability that an image identified as containing the object by the model actually contains the object?
1. A machine learning model is designed to predict whether a given email is spam or not. The model has an accuracy of 95%, a false positive rate of 2% and a false negative rate of 5%. If 10% of all emails received are spam, what is the probability that an email identified as spam the model is actually spam?
2. A company has a screening test for potential employees that has a false positive rate of 4% and a false negative rate of 2%. if 10% of the applicants are actually qualified for the job, what is the probability that a person who tests negative is actually qualified for the job?
3. A machine learning model is trained to predict whether a given image contains a certain object or not. The model has an accuracy of 90% a false positive rate of 5%, and false negative rate of 8%. if 20% of the images in the dataset contain the object, what is the probability that an image identified as containing the object by the model actually contains the object?
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