Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

Table 7

List of machine learning methods.

Method	Classification	Parameter and their value
Traditional machine learning	Decision tree (DT)	Criterion= gini, splitter= best, max depth=None, min samples split=2, min samples leaf =1, min weight fraction leaf=0.0,
	Logistical regression (LR)	Random state=1
	Naive Bayes (NB)	No kernel estimator
	Stochastic gradient descent (SGD)	Loss=hinge, penalty=l2
	Support vector machine (SVM)	Polynomial kernel function with exponent = , RBF kernel function with gamma = 0.01
Ensemble; learning	Random forest (RF)	Randomly sampled as candidates at each split = log2 + 1, max-depth=2, random state=0
	AdaBoost (AB)	Polynomial kernel function with exponent = , RBF kernel function with gamma = 0.01, n- estimators=100
	Gradient boosting (GB)	N-estimators =100, learning rate =1.0, max-depth=1, random state=0