DFp1_CV: Decision Forest preferred-1 algorithm: Model training with...

In seldas/Dforest: Decision Forest

Description

Decision Forest algorithm: Model training with Cross-validation Default is 5-fold cross-validation

Usage

DFp1_CV(X, Y, CV_fold = 5, stop_step = 4, param_T = 20, param_R = 5,
  param_L = 3, cp = 0.1, Filter = F, p_val = 0.05, Method = "bACC",
  Quiet = T, Grace_ACC = 0.05, imp_ACC_accu = 0.01, Grace_bACC = 0.05,
  imp_bACC_accu = 0.01, Grace_MCC = 0.05, imp_MCC_accu = 0.01,
  Grace_MIS = ceiling(0.05 * length(Y)), imp_MIS_accu = ceiling(0.01 *
  length(Y)))

Arguments

X	Training Dataset
Y	Training data endpoint
CV_fold	Fold of cross-validation (Default = 5)
stop_step	How many extra step would be processed when performance not improved, 1 means one extra step
param_T	Parameter T in IDF: Maximum tree number in Forest
param_R	parameter R in IDF: maximum occurrence of features
param_L	parameter L in IDF: 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_ACC	Grace Value in evaluation: the next model should have a performance (Accuracy) not bad than previous model with threshold
imp_ACC_accu	improvement in evaluation: adding new tree should improve the overall model performance (accuracy) by threshold
Grace_bACC	Grace Value in evaluation: (Balanced Accuracy)
imp_bACC_accu	improvement in evaluation: (Balanced Accuracy)
Grace_MCC	Grace Value in evaluation: (MCC)
imp_MCC_accu	improvement in evaluation: (MCC)
Grace_MIS	Grace Value in evaluation: (MIS)
imp_MIS_accu	improvement in evaluation: (MIS)

Value

.$accuracy: Overall training accuracy (Cross-validation)

.$pred: Detailed training prediction (Cross-validation)

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

Examples

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

  random_seq=sample(nrow(X))
  split_rate=3
  split_sample = suppressWarnings(split(random_seq,1:split_rate))
  Train_X = X[-random_seq[split_sample[[1]]],]
  Train_Y = Y[-random_seq[split_sample[[1]]]]

  CV_result = DFp1_CV(Train_X, Train_Y)

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