Hypothesis space
A hypothesis space is a set of candidate functions, models, parameter configurations, or rules that a learning or inference procedure can represent or consider.
Deep Learning with R, Third Edition
A hypothesis space is a predefined set of candidate operations, representations, or rules that a learning algorithm searches.
- How it is used
- Defines the possibilities searched when learning representations or selecting neural-network weights.
- How deep it goes
- Explains how architecture constrains representable functions and why gradient descent may miss a representable solution, without deriving the search mathematically.
- Examples
- Uses a three-layer Keras review classifier and a densely connected temperature-forecasting model compared with a commonsense baseline.
- What you can do after
- Evaluate/understand how architecture choices restrict learnable solutions and why a representable solution may still be difficult to find.
Read how Deep Learning with R, Third Edition explains hypothesis space →
Effective Data Analysis
A model class is a group of formulas and algorithms sharing characteristics and designed to solve a specific type of problem.
- How it is used
- Guides analysts from a real-world problem to a suitable class of statistical models.
- How deep it goes
- Defines model classes and contrasts their purposes, but stops short of detailing individual formulas, algorithms, or selection procedures.
- Examples
- Uses spending prediction, student outcomes, sales forecasting, performance clusters, and curriculum-withdrawal timing.
- What you can do after
- Evaluate a problem's outcome type and select an appropriate statistical-model class.
Read how Effective Data Analysis explains hypothesis space →
Related concepts
- kernel functionproduced by · A kernel function is a mathematical function that measures similarity between pairs of inputs, often representing an inner product in an implicit feature space.
- network topologyconfigured by · Network topology is the structural arrangement and connectivity relationships among the components of a network or system.
- machine learningused by · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
- model architectureequivalent to · The structural organization of a computational system’s components, relationships, and operations that determines how it processes information or behaves.
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