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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)
You can install the development version of ClassificationEnsembles like so:
devtools::install_github("InfiniteCuriosity/ClassificationEnsembles")ClassificationEnsembles will model the location of a car seat (Good, Medium or Bad) based on the other features in the Carseats data set
library(ClassificationEnsembles)
Classification(data = Carseats,
colnum = 7,
numresamples = 2,
do_you_have_new_data = "N",
how_to_handle_strings = 1,
save_all_trained_models = "N",
use_parallel = "N",
train_amount = 0.60,
test_amount = 0.20,
validation_amount = 0.20)The 20 models which are build automatically are:
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 dry_beams_small
classification data set:
ensemble_bag_rf_test_pred 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 dry_beans_small 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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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