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

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classification—function to perform classification analysis and return results to the user.

Description

classification—function to perform classification analysis and return results to the user.

Usage

Classification(
  data,
  colnum,
  numresamples,
  predict_on_new_data = c("Y", "N"),
  remove_VIF_above,
  scale_all_numeric_predictors_in_data,
  how_to_handle_strings = c(0("No strings"), 1("Strings as factors")),
  set_seed = c("Y", "N"),
  save_all_trained_models = c("Y", "N"),
  save_all_plots,
  stratified_random_column,
  use_parallel = c("Y", "N"),
  train_amount,
  test_amount,
  validation_amount
)

Arguments

data	a data set that includes classification data. For example, the Carseats data in the ISLR package
colnum	the number of the column. For example, in the Carseats data this is column 7, ShelveLoc with three values, Good, Medium and Bad
numresamples	the number of times to resample the analysis
predict_on_new_data	asks if the user has new data to be analyzed using the trained models that were just developed
remove_VIF_above	Removes columns with Variance Inflation Factors above the level chosen by the user
scale_all_numeric_predictors_in_data	Scales all numeric predictors in the original data
how_to_handle_strings	Option to convert strings to factor levels
set_seed	Option to set a seed
save_all_trained_models	Gives the user the option to save all trained models in the Environment
save_all_plots	Saves all plots in the user's chosen format
stratified_random_column	0 if no stratified random sampling, or column number for stratified random sampling
use_parallel	"Y" or "N" for parallel processing
train_amount	set the amount for the training data
test_amount	set the amount for the testing data
validation_amount	Set the amount for the validation data

Value

a full analysis, including data visualizations, statistical summaries, and a full report on the results of 35 models on the data

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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.

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Dry Beans small

Description

This is a stratified version of the full dry beans data set. This is about 7 percent of the full data set

Usage

dry_beans_small

Format

dry_beans_small A reduced version with 813 rows and 17 columns of the full data set available 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)

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

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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.

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.

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