Learn why time series forecasting is the best machine learning model for predicting rainfall.
Table of Contents
Question
A meteorological institute wants to predict, based on data from the past, how much it will rain next Sunday. What machine learning model is the best fit for this case?
A. Regression
B. Classification
C. Time series forecasting
Answer
C. Time series forecasting
Explanation
Time series forecasting enables predictions of numeric values at a future point in time.
Based on the given scenario, the meteorological institute wants to predict the amount of rainfall next Sunday based on past data. This is a time series forecasting problem, as it involves predicting future values based on historical data that is ordered chronologically. Time series forecasting is a type of supervised learning that involves training a model on historical data to make predictions about future events.
Regression is a type of supervised learning that is used to predict continuous numerical values, such as the amount of rainfall. Classification, on the other hand, is used to predict categorical values, such as whether it will rain or not. Therefore, regression and classification are not the best fit for this case.
In summary, the best machine learning model for predicting the amount of rainfall next Sunday based on past data is time series forecasting.
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