Topic 1 Question 16
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?
Classification
Reinforcement Learning
Recurrent Neural Networks (RNN)
Convolutional Neural Networks (CNN)
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コメント(15)
The answer is C. Use RNN because it is a time series analysis.
👍 25esuaaaa2021/06/05As Y2Data pointed out, your reasoning for choosing B does not make much sense.
Furthermore, Reinforcement Learning for this question does not make much sense to me. Reinforcement Learning is basically agent - task problems. You give the agent a task i.e. get out of a maze and then through trial and error and many many iterations the agent learns the correct way to perform the task. It is called Reinforcement because you ... well ... reinforce the agent, you reward the agent for correct choices and penalize for incorrect choices. In RL you dont use many / any previous data because the data is generated with each iteration I think.
👍 7george_ognyanov2021/10/09RNN is the "Preferred algorithm for sequential data like time series, speech, text, financial data, audio, video, weather and much more" since "It learns over time what information is important and what is not" because they "can remember important things about the input they received, which allows them to be very precise in predicting what’s coming next".
And Reinforcement Learning doesn't mean that the model will learn from new data (better explained by george_ognyanov).
👍 5mousseUwU2021/10/18
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