Package 'ClassificationEnsembles'

Carseats data

Description

This is the Carseats data as shown in the ISLR package.

Usage

Carseats

Format

Carseats A simulated data set with 400 observations and 11 rows

Sales

Unit sales (in thousands) at each location

CompPrice

Price charged by competitor at each location

Income

Community income level (in thousands of dollars)

Advertising

Local advertising budget for company at each location (in thousands of dollars)

Population

Population size in region (in thousands)

Price

Price company charges for car seats at each site

ShelveLoc

A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site

Age

Average age of the local population

Urban

A factor with levels No and Yes to indicate whether the store is in an urban or rural location

US

A factor with levels No and Yes to indicate whether the store is in the US or not

Source

ISLR data set, https://www.rdocumentation.org/packages/ISLR/versions/1.4/topics/Carseats

---

Comprehensive Multi-Class Classification Ensemble Pipeline Engine

Description

Comprehensive Multi-Class Classification Ensemble Pipeline Engine

Usage

Classification(
  dataset = NULL,
  target_col = NULL,
  palette_style = c("standard", "viridis", "modern"),
  config = ClassificationEnsemblesConfig(),
  verbose = TRUE
)

Arguments

dataset	A data frame containing the predictors and target variable.
target_col	Character string specifying the name of the target categorical variable factor.
palette_style	Character choice layout. Visual color scheme for plot vectors: "standard", "viridis", or "modern".
config	A pre-configured architecture configuration parameter matrix list from ClassificationEnsemblesConfig().
verbose	Logical. If TRUE, logs operational pipeline milestones to console. Default is TRUE.

Value

An object of class classification_pipeline containing evaluation metrics matrices, models, data dictionaries, and plots.

---

Configuration Parameters Matrix for Classification Pipeline

Description

Configuration Parameters Matrix for Classification Pipeline

Usage

ClassificationEnsemblesConfig(
  cv_folds = 5,
  train_pct = 0.75,
  vif_threshold = 5,
  transform_steps = c("nzv", "medianImpute", "center", "scale"),
  rf_tune = 5,
  glmnet_grid = expand.grid(alpha = seq(0, 1, length = 4), lambda = seq(0.001, 0.1,
    length = 5)),
  svm_tune_length = 5,
  nnet_tune_length = 5,
  tree_tune_length = 5,
  leverage_threshold = 999
)

Arguments

cv_folds	Integer. The number of cross-validation folds. Default is 5.
train_pct	Numeric. The training data proportion as a decimal between 0 and 1. Default is 0.75.
vif_threshold	Numeric. Variance Inflation Factor threshold for filtering multicollinear predictors. Default is 5.
transform_steps	Character vector. Preprocessing transforms to pass to caret. Default is c("nzv", "medianImpute", "center", "scale").
rf_tune	Integer. Tuning length parameter for Random Forest/Ranger models. Default is 5.
glmnet_grid	Optional custom tuning grid for Elastic Net models.
svm_tune_length	Integer. Tuning length parameter for SVM models. Default is 5.
nnet_tune_length	Integer. Tuning length parameter for Neural Network models. Default is 5.
tree_tune_length	Integer. Tuning length parameter for tree-based models (Rpart, C5.0, gbm). Default is 5.
leverage_threshold	Numeric. Outlier removal multiplier relative to mean hatvalues. 999 deactivates filter. Default is 999.

Value

A structured list containing isolated operational tuning parameters.

---

Run Fast Validation Classification Demo Suite

Description

Run Fast Validation Classification Demo Suite

Usage

ClassificationEnsemblesDemo()

Value

An invisible trained classification_pipeline object run over a standard medical diagnostic set.

---

Fast-Execution Configuration Matrix for Classification Verification

Description

Fast-Execution Configuration Matrix for Classification Verification

Usage

ClassificationEnsemblesFastConfig()

Value

A fast-track parameter subset list optimized for rapid package verification checks.

---

Cleveland Heart

Description

Posted by John Gennari, 3/13/90, This is Dr. Detrano's database modified to be a real MIXED dataset.

Usage

Cleveland_heart

Format

Cleveland_heart These are the original attributes: Attributes: 8 symbolic, 6 numeric. Age; sex; chest pain type (angina, abnang, notang, asympt) Trestbps (resting blood pres); cholesteral; fasting blood sugar < 120 (true or false); resting ecg (norm, abn, hyper); max heart rate; exercise induced angina (true or false); oldpeak; slope (up, flat, down) number of vessels colored (???); thal (norm, fixed, rever). Finally, the class is either healthy (buff) or with heart-disease (sick).

Source

https://archive-beta.ics.uci.edu/dataset/45/heart+disease/files?path=cleve.mod

The column names were corrected to be usable by R ('Chest_pain_type' instead of 'chest pain type'), removed the '<' symbol from a column name because the '<' symbol causes some errors reading the column names (such as tree models), removed three columns due to very high number of missing cells (noted as '?' in the original file.) Those three columns are 'Number_of_vessels_colored', 'Thal', and 'Resting_ECG'.

Age

Age of the subject

Sex

Sex of the subject, either male or female

Chest_pain_type

One of angina, abnang, notang, or asympt

Resting_blood_pressure

The resting blood pressure for the subject

Cholesteral

The patient's cholesterol

Fasting_blood_sugar

Binary, whether the fasting blood sugar is <120

Max_heart_rate

The maximum measured heart rate for the patient

Exercise_induced_angina

Binary, whether angina was induced due to exercise

Old_peak

Numeric value

Slope

Three levels: Down, flat, up

Sick_or_buff

Binary, is the patient sick or buff

Class

0 is healthy, 1,2,3,4 is sick.

---

Dry Beans

Description

A data set of dry beans

Usage

Dry_Beans

Format

Dry_Beans

Source

The full data set is on UCI: https://archive.ics.uci.edu/dataset/602/dry+bean+dataset

Area

The area of a bean zone and the number of pixels within its boundaries

Perimeter

Bean circumference is defined as the length of its border

MajorAxisLength

The distance between the ends of the longest line that can be drawn from a bean

MinorAxisLength

The longest line that can be drawn from the bean while standing perpendicular to the main axis

AspectRatio

Defines the relationship between MajorAxisLength and MinorAxisLength

Eccentricity

Eccentricity of the ellipse having the same moments as the region

ConvexArea

Number of pixels in the smallest convex polygon that can contain the area of a bean seed

EquivDiameter

Equivalent diameter: The diameter of a circle having the same area as a bean seed area

Extent

The ratio of the pixels in the bounding box to the bean area

Solidity

Also known as convexity. The ratio of the pixels in the convex shell to those found in beans.

Roundness

Calculated with the following formula: (4piA)/(P^2)

Compactness

Measures the roundness of an object

ShapeFactor1

Continuous value

ShapeFactor2

Continuous value

ShapeFactor3

Continuous value

ShapeFactor4

Continuous value

Class

(Seker, Barbunya, Bombay, Cali, Dermosan, Horoz and Sira)

---

Load and Decompress Package Pipeline Footprints

Description

Load and Decompress Package Pipeline Footprints

Usage

load_pipeline(file_path)

Arguments

file_path

Character path mapping to a serialized rds file.

Value

A deserialized classification_pipeline object.

---

Maternal Health Risk

Description

Data has been collected from different hospitals, community clinics, maternal health cares from the rural areas of Bangladesh through the IoT based risk monitoring system.

Usage

Maternal_Health_Risk

Format

Maternal_Health_Risk Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, HeartRate and RiskLevel. All these are the responsible and significant risk factors for maternal mortality, that is one of the main concern of SDG of UN.

Source

https://archive.ics.uci.edu/dataset/602/dry+bean+dataset

Age

Any ages in years when a women during pregnant.

SystolicBP

Upper value of Blood Pressure in mmHg, another significant attribute during pregnancy.

DiastolicBP

Lower value of Blood Pressure in mmHg, another significant attribute during pregnancy.

BS

Blood glucose levels is in terms of a molar concentration

BodyTemp

Body temperature in Farenheit

HeartRate

A normal resting heart rate

RiskLevel

Predicted Risk Intensity Level during pregnancy considering the previous attribute.

---

Plot Classification Pipeline Diagnostics Curves Canvas

Description

Plot Classification Pipeline Diagnostics Curves Canvas

Usage

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

Arguments

x	A classification_pipeline object generated by the Classification() engine.
pace_output	Logical. If TRUE, paces chart rendering frames via devAskNewPage. Default is TRUE.
...	Additional arguments passed to underlying plot methods.

---

Executive Production Projections and Probabilities Matrix

Description

Evaluates champion architectures against un-indexed datasets, appending classification projection labels and probability assignments.

Usage

predict_production(object, newdata)

Arguments

object	A trained classification_pipeline asset.
newdata	A data frame containing incoming raw feature data templates.

Details

Production Prediction Wrapper with Probability Metrics

Value

A data frame matching input dimensions appended with prediction classes and matching row probability matrices for the top 3 models.

---

Predict with Classification Pipeline Object

Description

Predict with Classification Pipeline Object

Usage

## S3 method for class 'classification_pipeline'
predict(object, newdata, model_name = "best", type = c("class", "prob"), ...)

Arguments

object	A trained classification_pipeline asset.
newdata	A data frame containing incoming data rows to score.
model_name	Character string specifying the target model from the leaderboard to score. Default is "best".
type	Character choice layout. Type of prediction to return: "class" for factor labels, "prob" for a probability assignment matrix. Default is "class".
...	Additional arguments passed to underlying predict methods.

Value

A factor vector or numeric probability matrix of classifications.

---

Print Classification Pipeline Summary Report

Description

Print Classification Pipeline Summary Report

Usage

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

Arguments

x	A classification_pipeline object generated by the Classification() engine.
...	Additional arguments passed to underlying print methods.

---

Compress and Save Trained Classification Pipeline Assets

Description

Compress and Save Trained Classification Pipeline Assets

Usage

save_pipeline(object, file_path)

Arguments

object	A trained classification_pipeline asset.
file_path	Character path mapping where to save the binary output.

Value

Invisible NULL.

---