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?
For an inventory prediction model with features such as region, location, historical demand, and seasonal popularity that needs to learn from new data on a daily basis, the most suitable algorithm would be Recurrent Neural Networks (RNN). RNNs are designed to handle sequential data and are capable of learning temporal dependencies, making them ideal for tasks involving time series analysis. The ability to update the model incrementally aligns well with the requirement to learn from new inventory data daily.
The answer is C. Use RNN because it is a time series analysis.
As 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.
My option is B. "You want the algorithm to learn from new inventory data on a daily basis". The implication is a feedback with reward or punishment, which can optimise the mode. But, all other options can only practice prediction against new data rather than learning knowledge from new data automatically.
It's C.
C - for time series
Correct answer is "C"
I'm not sure that daily basis means it is time series. It could mean updating the model daily. But I'll follow collective intelligence.
The question asks for "prediction model" classification and RL do not fit the bill CNN are used for vision so only answer left is C
I think it's D (CNN). I'd use C (RNN) in case we are predicting only based on historical demand (time series). However, as we are also taking region, location and seasonality popularity into consideration, it is not a time series problem anymore.
"algorithm to learn from new inventory data on a daily basis" = time series model , best option to deal with time series is forsure RNN , vote for C
RNN are a fit tool to work with time-series as this one, so C
ans: C
Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.
Went with C
go for C https://www.akkio.com/post/deep-learning-vs-reinforcement-learning-key-differences-and-use-cases#:~:text=Reinforcement%20learning%20is%20particularly%20well,of%20reinforcement%20learning%20in%20action.
C) The best choice for this scenario would be C. Recurrent Neural Networks (RNN). Rationale: The task at hand is a time-series prediction problem, where the goal is to predict future inventory levels based on historical data. RNNs are particularly well-suited for such tasks because they have “memory” and can learn patterns in sequential data1. Features like region, location, historical demand, and seasonal popularity can be used as input to the RNN. The network can then learn the temporal dependencies between these features and the inventory levels. RNNs can be trained incrementally, which means the model can be updated daily with new inventory data, allowing the model to adapt to changing trends and patterns
I would choose A. And it is only because the features already have time-series information (like demand). And it would be way easier to train XGBoost than RNN model.