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Question 103 - MLS-C01 discussion

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A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city as this is a prototype, only daily data from the last year is available

Which model is MOST likely to provide the best results in Amazon SageMaker?

A.
Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
Answers
A.
Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
B.
Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
Answers
B.
Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
C.
Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
Answers
C.
Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
D.
Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.
Answers
D.
Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.
Suggested answer: A

Explanation:

The Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm is a supervised learning algorithm that can perform both classification and regression tasks. It can also handle time series data, such as the air quality data in this case. The kNN algorithm works by finding the k most similar instances in the training data to a given query instance, and then predicting the output based on the average or majority of the outputs of the k nearest neighbors. The kNN algorithm can be configured to use different distance metrics, such as Euclidean or cosine, to measure the similarity between instances. To use the kNN algorithm on the single time series consisting of the full year of data, the Machine Learning Specialist needs to set the predictor_type parameter to regressor, as the output variable (air quality in parts per million of contaminates) is a continuous value. The kNN algorithm can then forecast the air quality for the next 2 days by finding the k most similar days in the past year and averaging their air quality values.

References:

Amazon SageMaker k-Nearest-Neighbors (kNN) Algorithm - Amazon SageMaker

Time Series Forecasting using k-Nearest Neighbors (kNN) in Python | by ...

Time Series Forecasting with k-Nearest Neighbors | by Nishant Malik ...

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asked 16/09/2024
Arvin Lee
39 questions
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