Mean Absolute Error (MAE) is a commonly used metric to evaluate the accuracy of a time series model. It measures the average magnitude of errors between predicted and actual values, providing a straightforward way to understand the model's performance. The formula for MAE is: ( \text{MAE} = \frac{1}{n} \sum_{i=1}^{n}
What is mean absolute error (MAE) in time series forecasting?
- Exploring Vector Database Use Cases
- How to Pick the Right Vector Database for Your Use Case
- Vector Database 101: Everything You Need to Know
- Master Video AI
- Mastering Audio AI
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does serverless impact application architecture design?
Serverless architecture significantly impacts application design by changing how developers approach scalability, resour
What is the scope of computer vision in the future?
The future scope of computer vision is vast, with advancements expected in automation, healthcare, and augmented reality
What is the role of exploration noise in reinforcement learning?
Exploration noise plays a crucial role in reinforcement learning by encouraging an agent to explore its environment rath