Supervised Model Families
Supervised Model Families groups the core ideas a learner needs at the 7 · modelling & training stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Linear modelsLinear models represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.
02Nearest-neighbour methodsNearest-neighbour methods represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.
03Decision treesDecision trees represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.
04Random forests and baggingRandom forests and bagging represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.
05Gradient boostingGradient boosting represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.
06Support vector machinesSupport vector machines is a practical concept within Supervised Model Families. It helps turn the broader workflow stage “7 · Modelling & Training” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
07Probabilistic / Bayesian modelsProbabilistic / Bayesian models represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.
08Neural networksNeural networks represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.