TABLE IV.

Optimization parameters for various feature sets of all Models

Feature Set	Model	Optimization Parameters
Spatiotemporal	ENS	Method: LogitBoost
	LR	Learner: Logistic, Lambda: 0.01169
	KNN	Num Neighbors: 3
	SVM	Solver: SMO, Kernel Function: Linear, Box Constraint: 41.478, Kernel Scale: 21.404
	Tree	Min Leaf Size: 19
Stride	ENS	Method: Bag
	LR	Learner: Logistic, Lambda: 0.01
	KNN	Distance: cityblock, Num Neighbors: 7
	SVM	Solver: SMO, Kernel Function: Linear, Box Constraint: 22.287, Kernel Scale: 23.763
	Tree	Min Leaf Size: 27
EDSS	ENS	Method: Bag
	LR	Learner: Logistic, Lambda: 0.0015
	KNN	Distance: chebychev, Num Neighbors: 20
	SVM	Solver: SMO, Kernel Function: Linear, Box Constraint: 575.38, Kernel Scale: 76.8350
	Tree	Min Leaf Size: 10
PRM	ENS	Method: Bag
	LR	Learner: Logistic, Lambda: 0.010281
	KNN	Distance: chebychev, Num Neighbors: 20
	SVM	Solver: SMO, Kernel Function: Linear, Box Constraint: 5.737, Kernel Scale: 10.624
	Tree	Min Leaf Size: 5
PRM + EDSS	ENS	Method: Bag
	LR	Learner: Logistic, Lambda: 0.0103
	KNN	Distance: chebychev, Num Neighbors: 20
	SVM	Solver: SMO, Kernel Function: Linear, Box Constraint: 5.737, Kernel Scale: 10.624
	Tree	Min Leaf Size: 5

ENS: Ensemble; LR: Logistic Regression; KNN: K-Nearest Neighbors; SVM: Support Vector Machine; Tree: Decision Tree; SMO: Sequential Minimal Optimization