DF_train {Dforest}

Decision Forest algorithm: Model training

Description

Decision Forest algorithm: Model training

Usage

DF_train(X, Y, stop_step = 5, Max_tree = 20, min_split = 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

min_split

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

imp_accu_criteria

if TRUE, model must have improvement in accumulated accuracy

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

.$Method: pass evaluating Methods used in training

.$cp: pass cp value used in training decision trees

Examples

  ##data(iris)
  X = iris[,1:4]
  Y = iris[,5]
  names(Y)=rownames(X)
  used_model = DF_train(X,factor(Y))