DFp1_train: Decision Forest preferred-1 algorithm: Model training

In seldas/Dforest: Decision Forest

Description

Decision Forest algorithm: Model training

Usage

DFp1_train(X, Y, stop_step = 5, Max_tree = 20, Max_feat = 5,
  min_leaf = 10, cp = 0.1, Filter = F, p_val = 0.05, Method = "bACC",
  Quiet = T, Grace_val = 0.05, imp_accu_val = 0.01,
  imp_accu_criteria = F)

Arguments

X	Training Dataset
Y	Training data endpoint
stop_step	How many extra step would be processed when performance not improved, 1 means one extra step
Max_tree	Maximum tree number in Forest
Max_feat	maximum occurrence of features in the forest
min_leaf	minimum leaves in tree nodes
cp	parameters to pruning decision tree, default is 0.1
Filter	doing feature selection before training
p_val	P-value threshold measured by t-test used in feature selection, default is 0.05
Method	Which is used for evaluating training process. MIS: Misclassification rate; ACC: accuracy
Quiet	if TRUE (default), don't show any message during the process
Grace_val	Grace Value in evaluation: the next model should have a performance (Accuracy, bACC, MCC) not bad than previous model with threshold
imp_accu_val	improvement in evaluation: adding new tree should improve the overall model performance (Accuracy, bACC, MCC) by threshold

Value

.$accuracy: Overall training accuracy

.$pred: Detailed training prediction (fitting)

.$detail: Detailed usage of Decision tree Features/Models and their performances

.$models: Constructed (list of) Decision tree models

Examples

  ##data(iris)
  X = iris[,1:4]
  Y = iris[,5]
  names(Y)=rownames(X)
  used_model = DF_train(X,Y,stop_step=4, Method = "MCC")

seldas/Dforest documentation built on May 30, 2019, 8:08 p.m.