What Is A Hierarchical Models?
Hierarchical models, also known as hierarchical statistical models, multilevel models or random-effects models, are tools for analysing data with a nested or grouped structure.Â
In machine learning, a hierarchical model is an approach that organises data and learning processes into layered structures. This structure reflects the inherent hierarchical nature of many real-world phenomena, allowing the model to capture complex relationships and dependencies between data points.
Hierarchical Models & Bayesian Hierarchical Modelling
Bayesian Hierarchical Modelling (BHM) is a powerful statistical approach that combines hierarchical modelling with Bayesian inference. It tackles data with inherent group structures by simultaneously analysing data at multiple levels and incorporating uncertainty estimations.
Here are the core principles of Bayesian Hierarchical Modelling:
- Hierarchical structure: Data is organised into groups, forming a hierarchy. For example, students within classes and classes within schools.
- Shared information: The model considers data from both individual units and their corresponding groups. This avoids treating each unit in isolation.
- Flexible parameters: Parameters can vary across groups, capturing group-specific effects. Instead of assuming a single average effect, BHM acknowledges variation.
- Bayesian approach: Probabilistic reasoning is used to estimate parameters and assess uncertainty. This provides more nuanced insights than traditional point estimates.
What Are The Two Components Of Hierarchical Models?
- Levels & Structure:
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- Levels: The core aspect of a hierarchical model is its layered structure. Data is organised into nested levels, representing increasing levels of abstraction or granularity. This could be students within classes, words within sentences, or genes within cells.
- Relationships: Between each level are defined relationships. These describe how the data points at one level connect and influence those at other levels. These relationships can be simple parent-child connections or more complex interactions.
- Models & Learning Processes:
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- Individual Models: Each level in the hierarchy has its own model or learning process. These models can be simple statistical models or even other machine learning algorithms.
- Interaction & Learning: These individual models interact and influence each other, allowing the overall model to capture complex relationships and dependencies in the data. For example, a model studying student achievement might have separate models for individual students, classrooms and schools, where the school-level model influences the classroom model, and the classroom model influences the individual student model.
The levels and structure define the organisation of the data and the relationships between elements. The models and learning processes operate on each level, utilising the structure to capture information and relationships across the hierarchy.
How Are Hierarchical Models Used In AI?
Hierarchical models are increasingly valuable tools in various areas of AI, offering several advantages over traditional flat models.
Improved Accuracy & Generalisability: By capturing the inherent hierarchical nature of real-world data, they can identify complex relationships and dependencies between components. This leads to more accurate predictions and models that generalise better to unseen data compared to flat models.
Enhanced Interpretability: The layered structure enables understanding how different factors on different levels contribute to the outcome. This makes interpreting the model’s decisions easier and more transparent, which is crucial for many AI applications.
Reduced Overfitting: Sharing information across levels allows hierarchical models to learn effectively even with limited data at each level. This reduces the risk of overfitting to specific details and enables them to generalise better.
