A RetroSearch Logo

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Search Query:

Showing content from https://github.com/InfiniteCuriosity/ClassificationEnsembles below:

InfiniteCuriosity/ClassificationEnsembles: Automatically Builds 25 Classification Models (15 Individual and 10 Ensembles of Model) From Classification Data

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:

  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 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

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

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4