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---
output: github_document
---
# ClassificationEnsembles <img src="man/figures/logo.png" align="right" height="139"/>
[](https://CRAN.R-project.org/package=ClassificationEnsembles) [](https://github.com/RussConte/ClassificationEnsembles/actions) \## Overview
`ClassificationEnsembles` provides an automated, institutional-grade multi-model framework for categorical targets. Architected to bring classification competitive pipelines to the exact same diagnostic level as `NumericEnsembles`, this package automates exploratory data analysis, manages class imbalance checks, prunes features via advanced Variance Inflation Factor (VIF) and logistic leverage filters, trains 11 concurrent base topologies, and builds 3 distinct non-linear stacking meta-blenders.
By presenting a 14-column performance standings leaderboard complete with **95% Confidence Intervals for both Accuracy and Macro-AUC**, `ClassificationEnsembles` delivers robust statistical validation out-of-the-box, allowing users to benchmark model code directly against elite peer-reviewed literature.
------------------------------------------------------------------------
## Academic Validation: Beating the Journals (*Nature*)
To demonstrate the power of the framework, users can execute benchmark replication scripts directly inside the console. In a recent study published in *Nature (Scientific Reports)*, authors evaluated the classic UCI Cleveland Heart Disease registry using a hybrid machine learning method, achieving an out-of-sample prediction accuracy of **0.952**, a specificity of **0.987**, and a sensitivity of **0.989** (Source: [`https://www.nature.com/articles/s41598-025-30895-5`](https://www.nature.com/articles/s41598-025-30895-5)).
By feeding raw biometric data into `ClassificationEnsembles` using our full transformation recipe, users can generate fully cross-validated leaderboards that meet or exceed these published results in under two minutes of execution.
------------------------------------------------------------------------
## Installation
Install the stable release from CRAN:
```{r Installation}
# install.packages("ClassificationEnsembles")
```
Or install the development version from GitHub:
```{r Install from Github}
# install.packages("devtools")
# devtools::install_github("InfiniteCuriosity/ClassificationEnsembles")
```
------------------------------------------------------------------------
## Two-Track Execution Workflows
### 1. The Express Track (Package Verification Check)
Optimized for rapid testing, package compliance checks, or local verification, the Express Track runs a 2-fold cross-validation partition over a balanced diagnostic matrix using a fast configuration wrapper.
```{r Demo of ClassificationEnsembles}
library(ClassificationEnsembles)
# Execute the integrated high-speed simulation pipeline end-to-end
fast_pipeline <- ClassificationEnsemblesDemo()
```
### 2. The Institutional Track (*Nature* Benchmark Replication)
The Institutional Track is the standard production pathway for empirical research. Below, we replicate the *Nature* paper framework by training on the full biometric footprint of the Cleveland Heart Disease dataset, explicitly isolating input parameters from target leaks.
```{r Nature Benchmark Replication}
library(ClassificationEnsembles)
# Load your local target registration matrix
Cleveland_Heart <- ClassificationEnsembles::Cleveland_heart
# Step 2: Establish the institutional configuration parameters matrix
biometric_config <- ClassificationEnsemblesConfig(
cv_folds = 5, # 5-Fold cross-validation matching the Nature track
train_pct = 0.80, # 80/20 train-test population split partition
vif_threshold = 10.0, # Multicollinearity feature pruning boundary
leverage_threshold = 3.0, # Automated logistic row outlier removal filter
transform_steps = c("nzv", "medianImpute", "center", "scale", "YeoJohnson"), # Normalization recipe
rf_tune = 5,
svm_tune_length = 5,
nnet_tune_length = 5,
tree_tune_length = 5
)
# Step 3: Run the competitive multi-model dispatch engine
Cleveland_pipeline <- Classification(
dataset = Cleveland_heart,
target_col = 'Class',
palette_style = "modern",
config = biometric_config,
verbose = TRUE
)
# Step 4: Audit your statistical validation dashboard and leaderboard!
print(Cleveland_pipeline)
```
------------------------------------------------------------------------
## Understanding the S3 Output Footprints
### Console Print Out Profile
When you pass a trained pipeline object into `print()`, the S3 interface prints a comprehensive 5-section diagnostic log to the console before rendering the performance standings leaderboard:
``` text
=========================================================================
CLASSIFICATION PIPELINE PROFILE SUMMARY METRICS
=========================================================================
[1. BASELINE DATA FACTOR HEAD]
# ... Prints head structure snapshot ...
[2. STRUCTURAL DATA DICTIONARY]
# ... Prints types, missing values rates, and unique observation counts ...
[3. PIPELINE AUTOMATED EXPLORATORY SUMMARY INSIGHTS]
# ... Prints structural checks for skews, cardinality risks, and class imbalances ...
[4. STATISTICAL POPULATION DESCRIPTIVE SUMMARY]
# ... Prints standard continuous numeric distribution summary matrices ...
[5. MULTICOLLINEARITY VIF FILTERS REPORT]
# ... Logs pruned vs kept feature elements ...
=========================================================================
CLASSIFICATION ARCHITECTURE LEADERBOARD
=========================================================================
Target Variable Factor Name: Sick_or_buff
Baseline No-Information Rate: 0.5424
Total Estimated Class Levels: 2 (Levels: buff, sick)
Top Performing Solution Pipelines Sorted By Macro-AUC:
Model Macro_AUC AUC 95% CI Lower AUC 95% CI Upper Accuracy Accuracy 95% CI Lower ...
Linear_GLM 0.9248 0.8621 0.9875 0.8475 0.7301
Logistic_Enet 0.9201 0.8555 0.9848 0.8475 0.7301
Neural_Network 0.9051 0.8286 0.9816 0.7966 0.6717
SVM_Radial 0.9028 0.8282 0.9774 0.8475 0.7301
...
```
------------------------------------------------------------------------
### Visual Graphics Portfolio
When you call `plot(Cleveland_pipeline)`, the S3 charting pipeline sequentially displays a collection of 11 multi-panel plots for deep diagnostics:
1. **Target Counts & Percentages:** Bar charts displaying frequency distributions and minority class bounds.
2. **Predictor Boxplots & Histograms:** Multi-panel continuous density facets broken down by class.
3. **Logistic Row Leverage:** Mapping individual leverage values against the outlier removal cutoff line.
4. **Feature Correlation Heatmap:** Color-coded layout tracking linear multicollinearity.
5. **Leaderboard & KPI Canvas:** Graphed error bars showing **95% DeLong AUC and Binomial Accuracy bounds**.
6. **Probability Deviance Residuals:** Box plots tracking log-loss penalties across all 13 algorithms.
7. **Information Tradeoff Space:** Visualizing log-loss error against the overfitting ratio to isolate the ideal model.
8. **Decile Probability Calibration:** Multi-panel reliability curves showing model calibration accuracy.
------------------------------------------------------------------------
## Production Scoring
Score incoming raw data tables using your top-performing champion model automatically:
```{r Profile scoring}
# Score new observations using the top leaderboard model automatically
class_projections <- predict(object = Cleveland_pipeline, newdata = incoming_patients, type = "class")
prob_matrices <- predict(object = Cleveland_pipeline, newdata = incoming_patients, type = "prob")
# Or leverage the executive wrapper to append top 3 model predictions simultaneously
production_table <- predict_production(object = Cleveland_pipeline, newdata = incoming_patients)
```
------------------------------------------------------------------------
## Package Architecture
``` text
ClassificationEnsembles/
├── R/
│ └── ClassificationEnsembles.R <- Core engine script, S3 printing/plotting, and automated validation
├── inst/
│ └── shiny-apps/
│ └── ClassificationApp/
│ └── app.R <- Interactive visualization portal app code
├── vignettes/
│ └── ClassificationEnsemblesManual.Rmd
├── DESCRIPTION <- Package metadata dependencies configuration matrix
└── README.Rmd <- Top-level github documentation template
```