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Question 182 - MLS-C01 discussion

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A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.

What type of machine learning model should be used?

A.
Classification month-to-month using supervised learning of the 200 categories based on claim contents.
Answers
A.
Classification month-to-month using supervised learning of the 200 categories based on claim contents.
B.
Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
Answers
B.
Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
C.
Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
Answers
C.
Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
D.
Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
Answers
D.
Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
Suggested answer: C

Explanation:

: Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.

Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.

References:

Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library

Time Series Forecasting Service -- Amazon Forecast -- Amazon Web Services

Amazon Forecast: Guide to Predicting Future Outcomes - Onica

Amazon Launches What-If Analyses for Machine Learning Forecasting ...

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asked 16/09/2024
Robert Rek
46 questions
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