Answer-first summary for fast verification
Answer: Long Short-Term Memory (LSTM)
The correct answer is **Long Short-Term Memory (LSTM)**. Here's why: - **Support Vector Machines (SVMs)**: Primarily designed for classification tasks, making them unsuitable for time series forecasting. - **Decision Trees**: While they can be applied to forecasting, they lack the capability to effectively model long-term dependencies in time series data. - **Linear Regression**: Suitable for some forecasting scenarios but fails to capture the complex patterns typical in time series data. - **Long Short-Term Memory (LSTM)**: A specialized type of recurrent neural network that excels in learning from sequential data. Its ability to remember and utilize long-term dependencies makes it the ideal choice for time series forecasting within Databricks MLlib.
Author: LeetQuiz Editorial Team
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In the context of a machine learning project focused on time series data for forecasting, which of the following algorithms, supported by Databricks MLlib, is most appropriate for the task?
A
Support Vector Machines
B
Decision Trees
C
Long Short-Term Memory (LSTM)
D
Linear Regression
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