Amazon Exam MLS-C01 Topic 2 Question 94 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 94
Topic #: 2

A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials.

...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.

...should the company retrain the model to meet these requirements?

ASet the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

BSet the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.

CUse 90% of the historical data for training Set the number of epochs to 20.

DSet the normalize_jabel hyperparameter to true. Set the number of classes to 2.

Show Suggested Answer

Suggested Answer: D

The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.

References:

pandas.DataFrame.groupby --- pandas 2.1.4 documentation

pandas.DataFrame.plot.bar --- pandas 2.1.4 documentation

Matplotlib - Bar Plot - Online Tutorials Library

by Dorathy at Jun 03, 2024, 04:36 PM

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1 months ago

Setting the normalize_label hyperparameter to true and the number of classes to 2 seems like a strange solution. I don't think that's going to help with the recall and precision requirements.

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2 months ago

Using 90% of the historical data for training and setting the number of epochs to 20 doesn't sound right to me. Shouldn't we be focusing on the model's performance metrics?

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Ellsworth

14 days ago

Using 90% of the historical data for training and setting the number of epochs to 20 doesn't sound right to me. Shouldn't we be focusing on the model's performance metrics?

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Sunny

1 months ago

B) Set the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.

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Alida

1 months ago

A) Set the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

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2 months ago

Hmm, the model needs to have a recall of at least 90% to meet the requirements. Option A seems like the right choice here.

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Sanjuana

1 months ago

User 2: Agreed, that seems like the best way to retrain the model to meet the requirements.

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Carin

2 months ago

User 1: I think we should go with option A to set the target_recall hyperparameter to 90%.

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Sean

2 months ago

I'm not sure. Maybe setting the targetprecision hyperparameter to 90% could also work.

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Florinda

2 months ago

I agree with Renea. Setting the target_recall hyperparameter to 90% seems like a good option.

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Renea

2 months ago

I think the company should retrain the model to meet the requirements.

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