Table 1.

Overview of model pros & cons, updated from Hastie (2003).

Characteristics	Neural network	SVM	Decision tree	RF	Generelized linear model	Gaussian mixture model	k-NN	Boosting
Model complexity	High	High	Low	Fair	Low	High	Low	Fair
Sensitivity to dataraji sparsity	High	High	Low	Fair	Low	High	High	Fair
Sensitivity to data bias	High	High	High	High	High	High	High	High
Interpretability	Poor	Poor	Fair	Poor	Good	Poor	Good	Poor
Predictive power	Good	Good	Poor	Good	Poor	Good	Poor	Good
Ability to extract linear combinations of features	Good	Good	Poor	Poor	Poor	Poor	Poor	Poor
Natural handling ofraji missing values	Poor	Poor	Good	Good	Poor	Good	Good	Good
Robustness to outliers in input space	Poor	Poor	Good	Good	Fair	Good	Good	Good
Computational scalability	Poor	Poor	Good	Good	Good	Poor	Poor	Good

SVM, Support Vector Machine; RF, Random Forest; k-NN, k-Nearest Neighbors.