Margin
A classification margin measures separation or confidence, expressed as distance from a decision boundary or as the correct class's score advantage over competing classes.
Machine Learning System Design
A measure of the separation between observations and the classifier boundary that determines their predicted classes.
- How it is used
- Uses metric sensitivity to sample noise to assess whether a classifier’s decision boundary is robust.
- How deep it goes
- Explains threshold sensitivity, noise testing, bootstrap distributions, and confidence bounds without deriving classifier geometry.
- Examples
- Demonstrates robustness using precision-recall distributions, bootstrapped datasets, sample sizes, and added normally distributed noise.
- What you can do after
- Diagnose boundary fragility by comparing noisy and unnoised metric distributions and reporting bootstrapped lower confidence bounds.
Machine Learning Algorithms in Depth
A margin is the distance between a classifier's decision boundary and the nearest training examples or class-separating boundaries.
- How it is used
- Uses geometric separation to select among correctly classifying decision boundaries in support vector machines.
- How deep it goes
- Derives a max-min distance objective and its quadratic-program formulation, then stops short of further optimization details.
- Examples
- Uses training examples, margin boundaries, support vectors, and a maximum-margin SVM diagram.
- What you can do after
- Evaluate classifiers by their nearest-example distance and understand why support vectors determine the maximum-margin solution.
Read how Machine Learning Algorithms in Depth explains margin →
Related concepts
- support vector machineused by · A support vector machine is a supervised-learning model that uses support vectors to determine a maximum-margin decision boundary or regression function, optionally using kernels to model nonlinear relationships.
- boostingused by · Boosting is an ensemble-learning technique that builds predictive models sequentially and combines them, with later models improving on weaknesses of earlier ones.
- error ratemeasures · Error rate is the proportion of evaluated operations, cases, or outcomes that produce an error or fail a specified criterion.
- support vectorused by · A support vector is a training example that contributes to determining a support vector machine’s learned prediction function or decision boundary.
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