Computes the proportion of matching terms in two vectors of the same length. Used to compute the accuracy for prediction on test set.

Description

Computes the proportion of matching terms in two vectors of the same length. Used to compute the accuracy for prediction on test set.

Usage

Acc(Vec1, Vec2)

Arguments

Value

Percentage of identical labels (accuracy)

Examples

Vec1 <- c(1,1,2,3,1)
Vec2 <- c(1,2,2,3,1)
Acc(Vec1,Vec2)

Main function of the package. It produces Classification Trees with Branch-Exclusive variables.

Description

Main function of the package. It produces Classification Trees with Branch-Exclusive variables.

Usage

BEST(Data, Size, VA)

Arguments

Value

A BEST object with is a list containing the resulting tree, row numbers for each regions and the split points

Examples

n <- 1000
Data <- BESTree::Data[1:n,]
d <- ncol(Data)-1
VA <- ForgeVA(d,1,0,0,0)
Size <- 50
Fit <- BESTree::BEST(Data,Size,VA)

Generates a random forest of BEST trees

Description

Generates a random forest of BEST trees

Usage

BESTForest(Data, VA, NoT = 50, Size = 50)

Arguments

Value

A list of BEST Objects (Random Forest)

Examples

n <- 500
Data <- BESTree::Data[1:n,]
d <- ncol(Data)-1
VA <- ForgeVA(d,1,0,0,0)
Size <- 50
NoT <- 10
Fit <- BESTree::BESTForest(Data,VA,NoT,Size)

Performs Bootstrap Aggregating of BEST trees

Description

Performs Bootstrap Aggregating of BEST trees

Usage

BaggedBEST(Data, VA, NoT = 50, Size = 50)

Arguments

Value

A list of BEST Objects

Examples

n <- 500
Data <- BESTree::Data[1:n,]
d <- ncol(Data)-1
VA <- ForgeVA(d,1,0,0,0)
Size <- 50
NoT <- 10
Fit <- BESTree::BaggedBEST(Data,VA,NoT,Size)

Data generated according to decision tree for simulation purposes

Description

Data generated according to decision tree for simulation purposes

Usage

Data

Format

A data frame with 10000 rows and 5 variables:

X_1

Binary predictor

X_2

Binary predictor

X_3

Continuous predictor between 0 and 1

X_4

Continuous predictor between 0 and 1

Y

The response variable

...

Emits prediction from a forest of BEST's

Description

Emits prediction from a forest of BEST's

Usage

FPredict(M, LFit)

Arguments

Value

A vector of predictions

Examples

n <- 500
Data <- BESTree::Data[1:n,]
d <- ncol(Data)-1
NewPoints <- BESTree::Data[(n+1):(n+11),1:d]
VA <- ForgeVA(d,1,0,0,0)
Size <- 50
NoT <- 10
Fit <- BESTree::BaggedBEST(Data,VA,NoT,Size)
Predictions <- BESTree::FPredict(NewPoints,Fit)

Data generated according to decision tree for simulation purposes

Description

Data generated according to decision tree for simulation purposes

Usage

Fit

Format

A typical list produced by the BEST function:

1

Tree structure indicating spliting variables, impurity of the region and split variable

2

List of splitting values

3

Observaton numbers in the respective regions

...

Quickly build the Available Variable list necessary for BEST This list contains details as to which variables is available for the partitioning. It also contains which variables are gating variables.

Description

Quickly build the Available Variable list necessary for BEST This list contains details as to which variables is available for the partitioning. It also contains which variables are gating variables.

Usage

ForgeVA(d, GV, BEV, Thresh = 0.5, Direc = 0)

Arguments

Value

The list containing the Variable Availability structure

Examples

#This function can be used to set up the variable availability structure.
#Suppose we want to fit a regular decision tree on a data set containing d predictors
d <- 10
VA <- ForgeVA(d,1,0,0,0)
#Suppose now that predictor x5 is a binary gating variable for x4
#such that x4 is available if x5 = 1
GV <- 5 #The gating variable
BEV <- 4 #The Branch-Exclusive variable
Tresh = 0.5 #Value between 0 and 1
Direc = 1 #X4 is available if X5 is bigger than Tresh
VA <- ForgeVA(d,GV,BEV,Tresh,Direc)

Classify a set of new observation points

Description

Classify a set of new observation points

Usage

MPredict(M, Fit)

Arguments

Value

The predicted class

Examples

n <- 500
Data <- BESTree::Data[1:n,]
d <- ncol(Data)-1
NewPoints <- BESTree::Data[(n+1):(n+11),1:d]
VA <- ForgeVA(d,1,0,0,0)
Size <- 50
Fit <- BESTree::BEST(Data,Size,VA)
Predictions <- BESTree::MPredict(NewPoints,Fit)

Classify a new observation point

Description

Classify a new observation point

Usage

Predict(Point, Fit)

Arguments

Value

The predicted class

Examples

n <- 500
Data <- BESTree::Data[1:n,]
NewPoint <- BESTree::Data[n+1,]
d <- ncol(Data)-1
VA <- ForgeVA(d,1,0,0,0)
Size <- 50
Fit <- BESTree::BEST(Data,Size,VA)
BESTree::Predict(NewPoint[1:d],Fit)

Uses a Validation Set to select the best trees within the list of pruned trees.

Description

Uses a Validation Set to select the best trees within the list of pruned trees.

Usage

TreePruning(Fit, VSet)

Arguments

Value

The shallower trees among trees wiht Highest accuracy. This replaces the first element in the BEST object list.

Examples

nv <- 50
ValData <- BESTree::Data[(1000+1):nv,]
Fit <- BESTree::Fit
Fit[[1]] <- BESTree::TreePruning(Fit,ValData)

Produces a variable important analysis using the mean decrease in node impurity

Description

Produces a variable important analysis using the mean decrease in node impurity

Usage

VI(Forest)

Arguments

Value

A vector of importance (size d)

Examples

n <- 500
Data <- BESTree::Data[1:n,]
d <- ncol(Data)-1
NewPoints <- BESTree::Data[(n+1):(n+11),1:d]
VA <- ForgeVA(d,1,0,0,0)
Size <- 50
NoT <- 10
Fit <- BESTree::BaggedBEST(Data,VA,NoT,Size)
VI <- BESTree::VI(Fit)