MLS-C01 · Question #119
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…
The correct answer is C. Forecasting using claim IDs and timestamps to identify how many claims in each category to. The final outcome among 200 categories is already know. No need to build a classification model. It's pure forecasting problem.
Question
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?
Options
- AClassification month-to-month using supervised learning of the 200 categories based on claim
- BReinforcement learning using claim IDs and timestamps where the agent will identify how many
- CForecasting using claim IDs and timestamps to identify how many claims in each category to
- DClassification with supervised learning of the categories for which partial information on claim
How the community answered
(26 responses)- A4% (1)
- B12% (3)
- C77% (20)
- D8% (2)
Explanation
The final outcome among 200 categories is already know. No need to build a classification model. It's pure forecasting problem.
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