AutoXGBoostdHurdleModel: AutoXGBoostdHurdleModel is generalized hurdle modeling framework

Description

AutoXGBoostdHurdleModel is generalized hurdle modeling framework

Usage

AutoXGBoostdHurdleModel(data, ValidationData = NULL, TestData = NULL,
  Buckets = 0, TargetColumnName = NULL, FeatureColNames = NULL,
  IDcols = NULL, TransformNumericColumns = NULL, ClassWeights = NULL,
  SplitRatios = c(0.7, 0.2, 0.1), TreeMethod = "hist",
  NThreads = max(1, parallel::detectCores() - 2),
  ModelID = "ModelTest", Paths = NULL, SaveModelObjects = TRUE,
  Trees = 1000, GridTune = TRUE, MaxModelsInGrid = 1,
  NumOfParDepPlots = 10, PassInGrid = NULL)

Arguments

data

Source training data. Do not include a column that has the class labels for the buckets as they are created internally.

ValidationData

Source validation data. Do not include a column that has the class labels for the buckets as they are created internally.

TestData

Souce test data. Do not include a column that has the class labels for the buckets as they are created internally.

Buckets

A numeric vector of the buckets used for subsetting the data. NOTE: the final Bucket value will first create a subset of data that is less than the value and a second one thereafter for data greater than the bucket value.

TargetColumnName

Supply the column name or number for the target variable

FeatureColNames

Supply the column names or number of the features (not included the PrimaryDateColumn)

IDcols

Includes PrimaryDateColumn and any other columns you want returned in the validation data with predictions

TransformNumericColumns

Transform numeric column inside the AutoCatBoostRegression() function

ClassWeights

Utilize these for the classifier model

SplitRatios

Supply vector of partition ratios. For example, c(0.70,0.20,0,10).

TreeMethod

Set to hist or gpu_hist depending on if you have an xgboost installation capable of gpu processing

NThreads

Set to the number of threads you would like to dedicate to training

ModelID

Define a character name for your models

Paths

A character vector of the path file strings. EITHER SUPPLY 1 file path or N file paths for N models

SaveModelObjects

Set to TRUE to save the model objects to file in the folders listed in Paths

Trees

Default 15000

GridTune

Set to TRUE if you want to grid tune the models

MaxModelsInGrid

Set to a numeric value for the number of models to try in grid tune

NumOfParDepPlots

Set to pull back N number of partial dependence calibration plots.

PassInGrid

Pass in a grid for changing up the parameter settings for catboost

Value

Returns AutoXGBoostRegression() model objects: VariableImportance.csv, Model, ValidationData.csv, EvalutionPlot.png, EvalutionBoxPlot.png, EvaluationMetrics.csv, ParDepPlots.R a named list of features with partial dependence calibration plots, ParDepBoxPlots.R, GridCollect, and the grid used

Examples

Run this code

# NOT RUN {
Output <- RemixAutoML::AutoXGBoostdHurdleModel(
  data,
  ValidationData = NULL,
  TestData = NULL,
  Buckets = 1,
  TargetColumnName = "Target_Variable",
  FeatureColNames = 4:ncol(data),
  IDcols = 1:3,
  TransformNumericColumns = NULL,
  ClassWeights = NULL,
  SplitRatios = c(0.7, 0.2, 0.1),
  TreeMethod = "hist",
  NThreads = max(1, parallel::detectCores()-2),
  ModelID = "ModelID",
  Paths = NULL,
  SaveModelObjects = TRUE,
  Trees = 1000,
  GridTune = FALSE,
  MaxModelsInGrid = 1,
  NumOfParDepPlots = 10,
  PassInGrid = NULL)
# }

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