Package 'NumericEnsembles'

Concrete - This is the strength of concrete daa set originally posted on UCI

Description

Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients.

Usage

Concrete

Format

Concrete A data frame with 1030 rows and 9 columns:

Cement

quantitative – kg in a m3 mixture – Input Variable

Blast_Furnace_Slag

quantitative – kg in a m3 mixture – Input Variable

Fly_Ash

quantitative – kg in a m3 mixture – Input Variable

Water

quantitative – kg in a m3 mixture – Input Variable

Superplasticizer

quantitative – kg in a m3 mixture – Input Variable

Coarse_Aggregate

quantitative – kg in a m3 mixture – Input Variable

Fine_Aggregate

quantitative – kg in a m3 mixture – Input Variable

Age

Day (1~365) – Input Variable

Strength

quantitative – MPa – Output Variable

Source

https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength

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Export Numeric Pipeline Outcomes and Graphs

Description

Export Numeric Pipeline Outcomes and Graphs

Usage

ExportNumericResults(pipeline_object = NULL, export_directory = NULL)

Arguments

pipeline_object	An object generated by the Numeric() engine.
export_directory	Character string path targeting file output folders.

Value

Invisible character string tracking file save directory destinations.

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Insurance - The data is from UCI

Description

This dataset contains detailed information about insurance customers, including their age, sex, body mass index (BMI), number of children, smoking status and region. Having access to such valuable insights allows analysts to get a better view into customer behaviour and the factors that contribute to their insurance charges.

Usage

Insurance

Format

Insurance A data frame with 1338 rows and 7 columns Credit to Bob Wakefield

Age

The age of the customer. (Integer)

Children

The number of children the customer has. (Integer)

Smoker

Whether or not the customer is a smoker. (Boolean)

Region

The region the customer lives in. (String)

Charges

The insurance charges for the customer. (Float)

Source

https://www.kaggle.com/datasets/thedevastator/prediction-of-insurance-charges-using-age-gender

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Launch Interactive NumericEnsembles Web Interface App

Description

Fires up a local instance of the interactive OLS vs. GLM tuning, diagnostic, and residual validation web dashboard system.

Usage

LaunchNumericApp()

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Load Serialized Numeric Pipeline Environment from Disk File

Description

Load Serialized Numeric Pipeline Environment from Disk File

Usage

load_pipeline(file_path)

Arguments

file_path

Character string tracking destination directory file path.

Value

A re-hydrated numeric_pipeline object.

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Core Performance Pipeline Engine for Continuous Data

Description

Core Performance Pipeline Engine for Continuous Data

Usage

Numeric(
  dataset = NULL,
  target_col = NULL,
  facet_col = "",
  color_col = "",
  stratify_col = "",
  palette_style = c("standard", "viridis", "modern"),
  config = NumericEnsemblesConfig(),
  verbose = TRUE
)

Arguments

dataset	A data.frame containing continuous target outputs and features.
target_col	Character string specifying the name of the target column.
facet_col	Character string specifying a column to facet EDA charts by. Default = "".
color_col	Character string specifying a column to color EDA charts by. Default = "".
stratify_col	Character string specifying a categorical column to anchor stratified sampling splits. Default = "".
palette_style	Character string choosing a color palette: "standard", "viridis", or "modern".
config	A pre-configured architecture parameter matrix block from NumericEnsemblesConfig().
verbose	Logical console logging status indicator. Default TRUE.

Value

An integrated execution bundle data object of class type numeric_pipeline.

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Configuration Parameters Matrix for Numeric Ensemble Pipeline

Description

Configuration Parameters Matrix for Numeric Ensemble Pipeline

Usage

NumericEnsemblesConfig(
  cv_folds = 5,
  train_pct = 0.6,
  vif_threshold = 5,
  transform_steps = c("nzv", "medianImpute", "center", "scale", "YeoJohnson"),
  rf_grid = NULL,
  glmnet_grid = expand.grid(alpha = seq(0, 1, length = 5), lambda = seq(0.001, 0.2,
    length = 10)),
  svm_tune_length = 8,
  mars_tune_length = 5,
  pcr_tune_length = 10,
  tree_tune_length = 10
)

Arguments

cv_folds	Integer. Number of cross-validation folds. Default 5.
train_pct	Decimal fraction between 0 and 1 for the training split. Default 0.60.
vif_threshold	Numeric. Maximum allowed Variance Inflation Factor. Default 5.
transform_steps	Character vector. Preprocessing transformations applied via caret.
rf_grid	Data frame or NULL. Explicit tuning grid for Random Forest.
glmnet_grid	Data frame. Hyperparameter grid for elastic net.
svm_tune_length	Integer. Total tuning resolution steps for svmRadial. Default 8.
mars_tune_length	Integer. Total tuning resolution steps for MARS. Default 5.
pcr_tune_length	Integer. Total tuning resolution steps for PCR. Default 10.
tree_tune_length	Integer. Total tuning resolution steps for decision trees. Default 10.

Value

A structured list containing isolated operational tuning parameters.

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Run a Demonstration of the Numeric Ensembles Pipeline

Description

Run a Demonstration of the Numeric Ensembles Pipeline

Usage

NumericEnsemblesDemo()

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Fast-Execution Configuration Matrix

Description

Fast-Execution Configuration Matrix

Usage

NumericEnsemblesFastConfig()

Value

A structured list containing optimized, fast-track hyperparameter settings.

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Plot Numeric Pipeline Performance Metrics and Visual Diagnostics

Description

Plot Numeric Pipeline Performance Metrics and Visual Diagnostics

Usage

## S3 method for class 'numeric_pipeline'
plot(x, pace_output = TRUE, ...)

Arguments

x	A numeric_pipeline object.
pace_output	Logical. If TRUE and session is interactive, paces chart pages. Default = TRUE.
...	Additional arguments.

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Generate Executive Production Projections with Row-Level 95 percent Confidence Intervals

Description

Generate Executive Production Projections with Row-Level 95 percent Confidence Intervals

Usage

predict_production(object, newdata)

Arguments

object	A trained 'numeric_pipeline' object.
newdata	A data.frame containing new data configurations to score.

Value

A data.frame structured with prediction vectors and interval metrics for the top 3 models.

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Predict with Numeric Pipeline Framework

Description

Predict with Numeric Pipeline Framework

Usage

## S3 method for class 'numeric_pipeline'
predict(object, newdata, model_name = "best", ...)

Arguments

object	A numeric_pipeline object.
newdata	A data.frame containing new data configurations to score.
model_name	Character string specifying the target model from the leaderboard to score. Default = "best".
...	Additional arguments.

Value

A numeric vector of predictions.

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Print Numeric Pipeline Summary Report

Description

Print Numeric Pipeline Summary Report

Usage

## S3 method for class 'numeric_pipeline'
print(x, ...)

Arguments

x	A numeric_pipeline object.
...	Additional arguments.

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Render Automated Quarto Numeric Executive Report

Description

Render Automated Quarto Numeric Executive Report

Usage

RenderExecutiveReport(pipeline_object = NULL, output_directory = getwd())

Arguments

pipeline_object	An object generated by the Numeric() engine.
output_directory	Character string specifying where to save the report files.

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Save Serialized Numeric Pipeline Environment to Disk File

Description

Save Serialized Numeric Pipeline Environment to Disk File

Usage

save_pipeline(object, file_path)

Arguments

object	A numeric_pipeline object.
file_path	Character string tracking destination directory file path.

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