Decomposition
Decomposition is the process of breaking down a complex problem or system into smaller, more manageable parts or components in order to better understand and solve it. In data science, decomposition is often used in time series analysis to break down a time series into its individual components, such as trend, seasonality, and noise. This allows for a better understanding of the underlying patterns and relationships within the data, and can lead to more accurate forecasting and modeling. Decomposition can also be used in machine learning, where it is often used to break down a complex model into simpler, more interpretable components, such as decision trees or linear models. This can help to improve the explainability and transparency of the model, and can also make it easier to identify and address any issues or errors that may arise.
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