ClassificationEnsembles

The goal of ClassificationEnsembles is to automatically conduct a thorough analysis of data that includes classification data. The user only needs to provide the data and answer a few questions (such as which column to analyze). ClassificationEnsembles fits 25 models (15 individual models and 10 ensembles of models). The package also returns 13 plots, five tables and a summary report sorted by accuracy (highest to lowest)

Installation

You can install the development version of ClassificationEnsembles like so:

r devtools::install_github("InfiniteCuriosity/ClassificationEnsembles")

Example

ClassificationEnsembles will model the location of a car seat (Good, Medium or Bad) based on the other features in the Carseats data set

``` r library(ClassificationEnsembles) Classification(data = ISLR::Carseats, colnum = 7, numresamples = 25, predictonnewdata = "N", setseed = "N", removeVIFabove = 5.00, scaleallnumericpredictorsindata = "N", howtohandlestrings = 1, savealltrainedmodels = "N", saveallplots = "N", useparallel = "Y", trainamount = 0.60, testamount = 0.20, validation_amount = 0.20) )

```

The 20 models which are build automatically are:

1. Bagged Random Forest
2. Bagging
3. C50
4. Ensemble BaggedCart
5. Ensemble Bagged Random Forest
6. Ensemble C50
7. Ensemble NaiveBayes
8. Ensemble Random Forest
9. Ensemble Ranger
10. Ensemble Support Vector Machines
11. Ensemble Trees
12. Linear
13. Naive Bayes
14. Partial Least Squares
15. Penalized Discrmininant Analysis
16. Random Forest
17. Ranger
18. RPart
19. Support Vector Machines
20. Trees

The 26 plots it returns automatically are:
1. Holdout accuracy / train accurcy by model, fixed scales 2. Residuals by model, free scales 3. Residuals by model, fixed scales 4. Classification error, free scales 5. Classification error, fixed scales 6. Accuracy data, free scales 7. Accuracy data, fixed scales 8. Accuracy by model, free scales 9. Accuracy by model, fixed scales 10. Histograms of numeric columns 11. Boxplots of numeric columns 12. Duration barchart 13. False negative rate free scales 14. False negative rate fixed scales 15. False positive rate, free scales 16. False positive rate, fixed scales 17. True negative rate, free scales 18. True negative rate, fixed scales 19. True positive rate, free scales 20. True positive rate, fixed scales 21. Over or underfitting barchart 22. Model accuracy barchart 23. Barchart of each feature vs target by percentage 24. Barchart of each feature vs target by value 25. Correlation of numeric data as circles and colors 26. Correlation of numeric data as numbers and colors

The 5 tables the package returns automatically are:
1. Head of the ensemble
2. Head of the data frame
3. Variance Inflation Factor of the numeric columns 4. Correlation of the data
5. Summary report, including accuracy, duration, overfitting, sum of diagonals

The package also returns 25 summary tables (sometimes called confusion matrices), one for each of the models. These can be found in the Console. For example, using the drybeamssmall classification data set:

ensemblebagrftestpred BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA BARBUNYA 21 0 0 0 0 0 0 BOMBAY 0 16 0 0 0 0 0 CALI 0 0 35 0 0 0 0 DERMASON 0 0 0 76 0 0 0 HOROZ 0 0 0 0 36 0 0 SEKER 0 0 0 0 0 48 0 SIRA 0 0 0 0 0 0 51

A data summary is also in the Console. Using drybeanssmall as an example: $Data_summary Eccentricity ConvexArea Extent Solidity roundness ShapeFactor4
Min. :0.2190 Min. : 20825 Min. :0.5802 Min. :0.9551 Min. :0.5718 Min. :0.9550
1st Qu.:0.7175 1st Qu.: 37052 1st Qu.:0.7240 1st Qu.:0.9859 1st Qu.:0.8320 1st Qu.:0.9941
Median :0.7642 Median : 45261 Median :0.7606 Median :0.9886 Median :0.8833 Median :0.9966
Mean :0.7517 Mean : 53997 Mean :0.7519 Mean :0.9874 Mean :0.8750 Mean :0.9952
3rd Qu.:0.8117 3rd Qu.: 62159 3rd Qu.:0.7887 3rd Qu.:0.9903 3rd Qu.:0.9191 3rd Qu.:0.9980
Max. :0.9082 Max. :229994 Max. :0.8325 Max. :0.9937 Max. :0.9879 Max. :0.9996

    y      

BARBUNYA: 79
BOMBAY : 31
CALI : 97
DERMASON:212
HOROZ :115
SEKER :121
SIRA :158