Non-Parametric Comparison¶

A quick reference guide comparing the strengths and weaknesses of different non-parametric models.

Comparison Table¶

Model	Strengths	Weaknesses	Best For
k-NN	Simple, no training phase	Slow inference, sensitive to scale	Baselines, simple spatial data
SVM (Kernel)	Powerful on complex boundaries	Slow to train on large data	Medium-sized complex datasets
Decision Tree	Highly interpretable	Prone to overfitting	Baselines, rule extraction
Random Forest	Robust, prevents overfitting	Can be slow to predict	General purpose tabular data
XGBoost	Extremely accurate, fast	Prone to overfitting if tuned poorly	Winning tabular competitions

KSB	Description	How This Addresses It
K4.2	Advanced ML techniques	Tree-based models, ensemble methods, KNN, SVM
K4.4	Trade-offs in selecting algorithms	Comparing parametric vs non-parametric approaches
S4	ML and optimisation	Hyperparameter tuning, ensemble construction, model selection
B1	Curiosity and creativity	Exploring when non-parametric methods outperform parametric ones
B5	Integrity in presenting conclusions	Avoiding overfitting; honest reporting of generalisation performance