splits_selection: Split dataset and select variables

In APML: An Approach for Machine-Learning Modelling

Split dataset and select variables

Description

Split dataset into training data and testing data and select variables based on relative importance.

Usage

splits_selection(data,split_ratio,split_seed,
feature_model,imbalance,nfolds,
RAN_type,RAN.seed,smote.seed,
xcol_enter,distribution)

Arguments

data	A data.frame used to build models
split_ratio	A numeric value indicating the ratio of total rows contained in each split. Must less than 1
split_seed	Random seed for splitting
feature_model	Name of model for feature selection. Currently, only allow "gbm" for gradient boosted tree, and "rf" for random forest
imbalance	Logical or "SMOTE"(for categorical response). True for balancing training data class counts via over/under-sampling when building the model. "SMOTE" for applying SMOTE and returning SMOTE training data.
nfolds	Number of folds for K-fold cross-validation. Default:5.
RAN_type	"both", "binominal" or "normal". "both" for generating both binominal and normal random terms for feature selection. "binominal" or "normal" only generate one specific type of random term. Categorical or continuous variables with relative importance greater than corresponding random term(s) will be selected.
RAN.seed	Random seed for random term(s)
smote.seed	Random seed for SMOTE. Only used if argument "imbalance"="SMOTE"
xcol_enter	A character vector of variables are required to enter the model, also called "forced entry". If xcol_enter contains all independent variables' names, it will not use random terms to select variables.
distribution	Distribution type. Must be one of: "AUTO", "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom". Defaults to AUTO.

Details

This function applys a technique to use random term to select variables. We consider variables with relative importance greater than random term as truly important variables.

Value

importance	A data.frame containing the relative importance scores of selected variables.
train_data	Training dataset. If "imbalance"="SMOTE", it returns the SMOTE training set.
test_data	Testing dataset.
raw_traindata	Same training dataset. If "imbalance"="SMOTE", it returns the original training set before SMOTE.

Note

This function is based on h2o package. In order to run this function, we need to run h2o.init() before using this function. The response variable should be the first column.

Examples

library(survival)
library(h2o)
library(performanceEstimation)
data("lung")
attach(lung)
data <- datatrans(lung,factor_dummy = 'dummy',rescale = TRUE)
data <- data[,c(3,1,2,4:14)]
h2o.init()
selection <- splits_selection(data,imbalance = 'SMOTE')
h2o.shutdown(prompt=FALSE)
Sys.sleep(2)

APML documentation built on May 12, 2022, 9:06 a.m.