You want to use supervised learning for automated resume screening, as in the example above. Which of the following statements about the Training Set are true? (Select all that apply.)
For this question shouldnt the right answers be
It should give examples of both the input A (resume) and the desired output B (whether to move forward with a candidate).
It will be used by the AI Team to train the supervise learning algorithm
The first option " It should give examples of the input A (resume) but not necessarily the desired output B (whether to move forward with a candidate)."
shouldnt both A and B be given in the training set so that the algorithm can learn which is meant to be the desired output ?
need clarity on this or is training set something along the lines where there will be a multiple sets and each will have its own way like some may have only A and some may have only B and some may have a mix of both A and B
Welcome to the community.
The idea behind supervised learning is to learn the mapping between your inputs(in this case A(resume)) to the output(s)(B in this case the decision to move forward with the candidate). You cannot do supervised learning without the output(s) for each example in your training set.
You can revise the course material and retake the quiz to figure it out. If you have any questions as to why a specific choice is correct(or incorrect) you can get it clarified.
The training and test dataset in a supervised learning problem has both the inputs and outputs. The presence of the outputs for each example is what makes it “Supervised learning”. You train your models on the training dataset and evaluate them on the test dataset whose examples haven’t been seen in training.
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Hello! For whom else may this question arise, please check the video “Working with an AI Team”, from 04:00 on. It explains what is the training set, the test set, the difference between one and another and it is very clear on how the test set is used within the machine learning system.