autoDataprep: Automatic data preparation for ML algorithms
In daya6489/DriveML: Self-Drive Machine Learning Projects

Description Usage Arguments Details Value See Also Examples

Final data preparation before ML algorithms. Function provides final data set and highlights of the data preparation

autoDataprep(
  data,
  target = NULL,
  missimpute = "default",
  auto_mar = FALSE,
  mar_object = NULL,
  dummyvar = TRUE,
  char_var_limit = 12,
  aucv = 0.02,
  corr = 0.99,
  outlier_flag = FALSE,
  interaction_var = FALSE,
  frequent_var = FALSE,
  uid = NULL,
  onlykeep = NULL,
  drop = NULL,
  verbose = FALSE
)

`data`	[data.frame \| Required] dataframe or data.table
`target`	[integer \| Required] dependent variable (binary or multiclass)
`missimpute`	[text \| Optional] missing value imputation using mlr misimpute function. Please refer to the "details" section to know more
`auto_mar`	[character \| Optional] identify any missing variable which are completely missing at random or not (default FALSE). If TRUE this will call autoMAR()
`mar_object`	[character \| Optional] object created from autoMAR function
`dummyvar`	[logical \| Optional] categorical feature engineering i.e. one hot encoding (default is TRUE)
`char_var_limit`	[integer \| Optional] default limit is 12 for a dummy variable preparation. e.g. if gender variable has two different value "M" and "F", then gender has 2 levels
`aucv`	[integer \| Optional] cut off value for AUC based variable selection
`corr`	[integer \| Optional] cut off value for correlation based variable selection
`outlier_flag`	[logical \| Optional] to add outlier features (default is FALSE)
`interaction_var`	[logical \| Optional] bulk interactions transformer for numerical features
`frequent_var`	[logical \| Optional] frequent transformer for categorical features
`uid`	[character \| Optional] unique identifier column if any to keep in the final data set
`onlykeep`	[character \| Optional] only consider selected variables for data preparation
`drop`	[character \| Optional] exclude variables from the dataset
`verbose`	[logical \| Optional] display executions steps on console(default is FALSE)

Missing imputation using impute function from MLR

MLR package have a appropriate way to impute missing value using multiple methods. #'

mean value for integer variable
median value for numeric variable
mode value for character or factor variable

optional: You might be interested to impute missing variable using ML method. List of algorithms will be handle missing variables in MLR package listLearners("classif", check.packages = TRUE, properties = "missings")[c("class", "package")]

Feature engineering

missing not completely at random variable using autoMAR function
date transfomer like year, month, quarter, week
frequent transformer counts each categorical value in the dataset
interaction transformer using multiplication
one hot dummy coding for categorical value
outlier flag and capping variable for numerical value

Feature reduction

zero variance using nearZeroVar caret function
pearson's correlation value
auc with target variable

list output contains below objects

complete_data: complete dataset including new derived features based on the functional understanding of the dataset
master_data: filtered dataset based on the input parameters
final_var_list: list of master variables
auc_var: list of auc variables
cor_var: list of correlation variables
overall_var: all variables in the dataset
zerovariance: variables with zero variance in the dataset

impute

#Auto data prep
traindata <- autoDataprep(heart, target = "target_var", missimpute = "default",
dummyvar = TRUE, aucv = 0.02, corr = 0.98, outlier_flag = TRUE,
interaction_var = TRUE, frequent_var = TRUE)
train <- traindata$master_data