library(readxl)
library(dplyr)
## 
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(knitr)
library(kableExtra)
## 
## Adjuntando el paquete: 'kableExtra'
## The following object is masked from 'package:dplyr':
## 
##     group_rows
library(Amelia)
## Cargando paquete requerido: Rcpp
## ## 
## ## Amelia II: Multiple Imputation
## ## (Version 1.8.3, built: 2024-11-07)
## ## Copyright (C) 2005-2025 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##
rut<-"Dataset_IA_corte_II.xlsx"
datos<-read_excel(rut)

Introducción

La Inteligencia Artificial (IA) ha transformado profundamente múltiples sectores, consolidándose como una herramienta esencial para enfrentar desafíos complejos y mejorar la eficiencia en diversas industrias. En el corazón de la IA están los algoritmos que capacitan a las máquinas para aprender de grandes cantidades de datos y tomar decisiones informadas. Cuando estos algoritmos se entrenan correctamente, pueden clasificar información, hacer predicciones precisas y descubrir patrones en escenarios donde los métodos tradicionales resultan ineficaces. No obstante, la efectividad de los modelos de IA depende en gran medida de la selección del algoritmo adecuado, la plataforma de desarrollo empleada y el tipo de problema que se busca resolver.

En este analisis estadistico descriptivo se estudiara una base de datos que contiene el desempeño de varios algoritmos de inteligencia artificial sobre un problema especifico, que contiene el desempeño, la precisión, tiempo de entrenamiento y otras caracteristicas correspondientes al proceso de cada algoritmo, caracteristicas que se compararan con el objetivo de lograr el objetivo planteado.

Objetivo

En este análisis exploratorio de datos (EDA), se buscará analizar la variación en la media de todas las variables en función del tipo de algoritmo de Inteligencia Artificial utilizado enfocados en un tipo de problema especifico, con el fin de identificar diferencias significativas en el desempeño de los modelos y evaluar qué algoritmos presentan una mayor estabilidad en sus resultados.

kable(datos, caption="Base de datos, Inteligencia Artificial(IA)") %>%
  kable_styling(full_width=F) %>%
  column_spec(2, width="20em") %>%
  scroll_box(width="900px", height="450px")
Base de datos, Inteligencia Artificial(IA)
Algorithm Framework Problem_Type Dataset_Type Accuracy Precision Recall F1_Score Training_Time Date
SVM Scikit-learn Regression Time Series 0.6618051 0.6929447 NA 0.4426950 4.9785924 2023-03-08 11:26:21
K-Means Keras Clustering Time Series 0.7443216 0.4900292 0.8766533 0.4414046 NA 2023-03-09 11:26:21
Neural Network Keras Clustering Image 0.8852037 0.5948056 0.9685424 0.9644707 3.2825938 2023-03-10 11:26:21
SVM Keras Clustering Text 0.8416477 0.8424142 0.8748388 0.7041523 4.0416289 2023-03-11 11:26:21
SVM Scikit-learn Regression Tabular 0.7229514 0.6856109 0.3010956 0.6456472 3.6039908 2023-03-12 11:26:21
K-Means PyTorch Regression Image 0.6368133 0.6255330 7.4548096 0.8865271 3.0064753 2023-03-13 11:26:21
Neural Network PyTorch Regression Text 0.9985623 0.6366858 0.3357948 0.9014956 NA 2023-03-14 11:26:21
Neural Network Scikit-learn Regression Image 0.7130907 0.6756681 0.4803251 0.5993146 2.3283453 2023-03-15 11:26:21
SVM Keras Regression Time Series NA 0.8710099 0.3416673 0.8161708 3.4064529 2023-03-16 11:26:21
Random Forest Keras Regression Text 0.5818119 0.9352508 NA 0.8626737 3.4199049 2023-03-17 11:26:21
SVM PyTorch Regression Image 0.8974048 9.7320081 0.7806129 0.7927904 1.9283008 2023-03-18 11:26:21
SVM Keras Clustering Image 0.8468411 0.8721420 0.3801413 0.4909570 4.7142907 2023-03-19 11:26:21
SVM TensorFlow Clustering Tabular 0.6103848 0.5892441 0.5686872 0.9255299 0.9200495 2023-03-20 11:26:21
SVM PyTorch Clustering Image 0.5411905 0.8128808 0.6193656 0.7234567 2.5517613 2023-03-21 11:26:21
K-Means Keras Clustering Text 0.8402497 0.6625619 0.5583371 0.5694835 3.4853315 2023-03-22 11:26:21
Neural Network PyTorch Regression Text NA 0.5528024 0.3847175 0.6551369 3.5159654 2023-03-23 11:26:21
K-Means TensorFlow Classification Tabular 0.6366298 0.9045229 0.5932635 0.4225427 3.2783309 2023-03-24 11:26:21
K-Means PyTorch Regression Text 0.9754318 0.4230558 0.8258246 0.4767201 1.4489122 2023-03-25 11:26:21
K-Means PyTorch Classification Time Series 0.5755289 0.9410572 0.3497054 0.8593281 0.8654122 2023-03-26 11:26:21
SVM PyTorch Clustering Text 0.7161674 0.6768865 0.3561260 0.4000070 3.2161076 2023-03-27 11:26:21
Random Forest PyTorch Regression Text 9.7180796 0.7823209 0.5483399 0.6499395 3.0365804 2023-03-28 11:26:21
Neural Network TensorFlow Clustering Image 0.7098637 0.7956124 0.9592080 0.7135061 0.9788445 2023-03-29 11:26:21
Random Forest Scikit-learn Regression Image 0.8192630 0.9370706 0.7680009 0.4327807 3.5551818 2023-03-30 11:26:21
K-Means PyTorch Clustering Text 0.6987972 0.7820018 0.7750690 0.9838469 2.3303428 2023-03-31 11:26:21
K-Means PyTorch Classification Tabular 0.6371076 0.7683602 0.5533440 0.5356752 3.3720269 2023-04-01 11:26:21
Random Forest Keras Classification Image 0.9919888 0.4399912 0.7155626 0.5825192 4.2030987 2023-04-02 11:26:21
Random Forest PyTorch Classification Tabular 0.7046670 0.7110448 0.3070918 0.5823655 0.9316693 2023-04-03 11:26:21
Random Forest Keras Regression Text 0.9470496 0.4901014 0.7452672 0.5382500 0.1938099 2023-04-04 11:26:21
K-Means Scikit-learn Classification Text 0.6149773 0.8424603 0.9393009 0.4008843 3.9176027 2023-04-05 11:26:21
K-Means TensorFlow Regression Time Series NA 0.7073332 0.7288014 0.8376069 3.0875174 2023-04-06 11:26:21
Neural Network TensorFlow Classification Image 0.5155670 0.8081367 0.9115890 0.9801073 3.5282339 2023-04-07 11:26:21
Neural Network Scikit-learn Classification Text 0.8258334 0.4250037 NA 0.5345761 4.2069594 2023-04-08 11:26:21
K-Means TensorFlow Regression Time Series 0.6842632 0.4508752 0.3843909 0.7978283 4.0337736 2023-04-09 11:26:21
Random Forest Scikit-learn Regression Image NA 0.8297940 0.9317173 8.4513780 4.8085087 2023-04-10 11:26:21
Random Forest Scikit-learn Classification Text 0.7366050 0.4432506 0.3465107 0.9090552 2.7260819 2023-04-11 11:26:21
Neural Network Scikit-learn Regression Text 0.9840967 0.4427540 0.6737772 0.6535775 2.4936286 2023-04-12 11:26:21
K-Means TensorFlow Classification Image 5.9276276 NA 0.3994960 0.5817585 2.0692473 2023-04-13 11:26:21
Neural Network Keras Regression Image 0.9343116 0.9739008 0.3081946 0.5951771 0.8530861 2023-04-14 11:26:21
Neural Network Scikit-learn Clustering Image 0.8882984 0.8425050 0.5954240 0.8275728 NA 2023-04-15 11:26:21
SVM PyTorch Clustering Text 0.8854609 0.6119508 0.5065285 0.8900677 1.4573614 2023-04-16 11:26:21
SVM TensorFlow Clustering Text 0.9223916 0.5779213 0.6402003 0.5089684 4.6144068 2023-04-17 11:26:21
SVM Scikit-learn Clustering Time Series 0.8805120 0.6098219 0.7040399 NA 2.9576349 2023-04-18 11:26:21
Random Forest Scikit-learn Clustering Tabular 0.8131102 0.8647921 NA 0.9411641 3.0049169 2023-04-19 11:26:21
K-Means PyTorch Classification Image 0.5656224 0.7968224 0.3861023 0.8840161 1.8391308 2023-04-20 11:26:21
K-Means Keras Clustering Text NA 0.5111173 0.6910497 0.9909150 0.3547478 2023-04-21 11:26:21
K-Means Keras Clustering Text 0.9604239 0.5044656 0.5402171 0.8525490 0.2555445 2023-04-22 11:26:21
K-Means Keras Clustering Image 0.8083252 0.4590374 0.8104214 0.6359171 2.1743422 2023-04-23 11:26:21
SVM Scikit-learn Clustering Tabular 0.8982686 0.7961816 0.7566042 0.7543827 0.5143704 2023-04-24 11:26:21
Random Forest Scikit-learn Regression Image 0.7407612 0.8586236 0.8919231 0.7966086 NA 2023-04-25 11:26:21
Random Forest Scikit-learn Classification Tabular 0.5586541 0.5590279 0.7847450 0.4470735 4.4167677 2023-04-26 11:26:21
SVM TensorFlow Clustering Text 0.5625929 0.4125670 0.6009518 0.7266982 4.4262826 2023-04-27 11:26:21
Random Forest PyTorch Clustering Tabular 0.8427826 0.4493030 0.7710766 0.8255925 3.3293225 2023-04-28 11:26:21
SVM Scikit-learn Clustering Time Series 0.7151529 0.9807160 0.4927668 0.5003928 1.1350360 2023-04-29 11:26:21
K-Means PyTorch Classification Text 0.6002624 0.5772669 0.5144194 0.8683790 4.3286845 2023-04-30 11:26:21
SVM PyTorch Regression Time Series 0.7457973 0.8615339 0.8522896 NA 4.4418578 2023-05-01 11:26:21
K-Means Keras Classification Image 0.5321045 0.7747981 NA 0.9713331 1.0636026 2023-05-02 11:26:21
K-Means TensorFlow Regression Image 0.7909857 0.6291638 0.8588662 0.4254534 3.7133878 2023-05-03 11:26:21
Neural Network Scikit-learn Clustering Image 0.6344967 0.5234124 0.8756957 0.5591957 1.5039458 2023-05-04 11:26:21
SVM Keras Clustering Tabular 0.8987796 0.4728319 0.9002936 0.7609323 4.0329375 2023-05-05 11:26:21
Neural Network Keras Classification Text 0.6551810 0.7690078 0.9416447 0.5779359 4.9864657 2023-05-06 11:26:21
SVM Scikit-learn Classification Image 0.7276101 0.8647803 0.6016897 0.8286545 0.2471274 2023-05-07 11:26:21
SVM PyTorch Regression Image 0.5058103 0.7863426 0.5232068 0.8554032 4.4970927 2023-05-08 11:26:21
Neural Network Keras Regression Text 0.5362234 0.7181813 0.7075391 0.4615096 3.1508896 2023-05-09 11:26:21
Neural Network PyTorch Classification Image 0.6962468 0.4251707 0.5598207 0.7083127 4.8695748 2023-05-10 11:26:21
SVM TensorFlow Regression Time Series 0.7399694 0.9810933 0.7207519 0.7053343 2.3785466 2023-05-11 11:26:21
Random Forest PyTorch Classification Text 0.8000103 0.8792285 0.7939101 0.6215685 4.2522009 2023-05-12 11:26:21
K-Means TensorFlow Regression Text 0.6458313 0.5756932 0.7818835 0.9597549 0.4057309 2023-05-13 11:26:21
Neural Network PyTorch Classification Text 0.8474909 0.9879822 0.5621870 0.8965038 1.7428673 2023-05-14 11:26:21
K-Means Keras Classification Time Series 0.9300612 0.7611290 0.4168021 0.8183256 0.4283810 2023-05-15 11:26:21
Random Forest Keras Classification Text 0.8899255 0.7494536 0.6013705 0.8285960 4.8791814 2023-05-16 11:26:21
Random Forest TensorFlow Regression Time Series 0.5198094 0.8488439 0.3998159 0.6770297 4.1031721 2023-05-17 11:26:21
Random Forest Scikit-learn Regression Text 0.7402535 0.8870619 0.9230679 0.9525967 4.2774824 2023-05-18 11:26:21
Neural Network TensorFlow Regression Tabular 0.5524651 0.7938872 0.5421142 0.8167573 4.6960296 2023-05-19 11:26:21
Random Forest TensorFlow Classification Image 0.6210225 0.4768574 NA 0.8373886 0.5170071 2023-05-20 11:26:21
Neural Network PyTorch Regression Tabular 0.9933313 0.6029605 0.3178134 0.9170145 NA 2023-05-21 11:26:21
Random Forest PyTorch Regression Image 0.5712478 0.9568502 0.7520757 0.5644430 0.4480710 2023-05-22 11:26:21
K-Means TensorFlow Classification Time Series 0.7494441 0.5347694 0.7458316 0.8842425 1.1328862 2023-05-23 11:26:21
K-Means Keras Clustering Tabular 0.8090779 0.6233002 0.5384229 NA 1.2238923 2023-05-24 11:26:21
SVM PyTorch Classification Text 0.8512325 0.6592461 0.3501983 0.6072052 2.3986253 2023-05-25 11:26:21
K-Means Scikit-learn Regression Image 0.7798243 0.6636430 0.5867402 0.6013663 1.4159546 2023-05-26 11:26:21
SVM PyTorch Classification Image 0.5048854 0.7677637 0.5178522 0.9871153 0.5947927 2023-05-27 11:26:21
K-Means PyTorch Clustering Tabular 0.6632307 0.9658455 0.7739844 0.9139223 0.9207063 2023-05-28 11:26:21
Neural Network Scikit-learn Classification Time Series 0.7588558 0.5444156 0.7240455 0.8207019 0.8216503 2023-05-29 11:26:21
K-Means TensorFlow Regression Tabular 0.5439332 0.4729008 0.5552156 0.8362341 4.8680763 2023-05-30 11:26:21
SVM Scikit-learn Clustering Time Series 0.6753135 0.5184823 0.4525248 NA 3.8205333 2023-05-31 11:26:21
SVM TensorFlow Regression Time Series 0.5166016 0.9321549 0.9916252 0.9682544 4.8420312 2023-06-01 11:26:21
Random Forest TensorFlow Clustering Image 0.5392892 0.7874865 0.6178011 0.6977553 2.2547284 2023-06-02 11:26:21
Neural Network TensorFlow Clustering Tabular 0.6984616 0.5715441 0.7817920 0.6283106 1.4640056 2023-06-03 11:26:21
K-Means Scikit-learn Classification Tabular 0.5663579 0.8895682 0.3983871 0.4978212 4.0116367 2023-06-04 11:26:21
Random Forest TensorFlow Classification Time Series 0.7837704 0.9168220 NA 0.8717234 1.6986442 2023-06-05 11:26:21
K-Means Keras Regression Tabular 0.8447325 0.9079086 0.3192757 0.8406664 1.5669785 2023-06-06 11:26:21
K-Means Scikit-learn Classification Text 0.9002933 0.9513559 0.6538186 0.6306130 1.2392794 2023-06-07 11:26:21
Random Forest Keras Classification Text 0.6000751 0.5513446 0.9748122 0.4151160 0.7354270 2023-06-08 11:26:21
Random Forest TensorFlow Clustering Tabular 0.5837413 0.8530252 0.5689447 0.9033984 1.3566690 2023-06-09 11:26:21
Random Forest PyTorch Classification Tabular 0.5522839 0.6763237 0.3272972 0.4068508 1.8410668 2023-06-10 11:26:21
Random Forest TensorFlow Clustering Time Series 0.8182151 0.9051991 0.3216691 0.8222199 3.4025716 2023-06-11 11:26:21
Random Forest Keras Clustering Text 8.5323786 0.8370944 NA 0.9821543 0.4065677 2023-06-12 11:26:21
K-Means Keras Clustering Text 0.5157931 0.8658685 0.4120175 0.6625968 1.1314840 2023-06-13 11:26:21
Random Forest Scikit-learn Regression Image 0.9681061 0.7936971 0.3163465 0.5409840 4.0642158 2023-06-14 11:26:21
Neural Network PyTorch Regression Time Series 5.2598564 0.5064573 0.8293495 0.8229226 0.8199174 2023-06-15 11:26:21
SVM Scikit-learn Regression Image 0.7706482 0.7270162 0.6209661 0.8902769 1.7789612 2023-06-16 11:26:21
Random Forest Keras Clustering Text 0.8545303 0.9908018 0.5024713 0.7278582 4.3368287 2023-06-17 11:26:21
Random Forest TensorFlow Regression Time Series 0.9354846 0.9624328 NA 0.9802212 NA 2023-06-18 11:26:21
K-Means PyTorch Regression Tabular 0.8570435 0.4259042 0.3812973 0.4310012 0.5054105 2023-06-19 11:26:21
Random Forest TensorFlow Classification Time Series 0.9008640 0.4988889 0.9691432 0.7028774 2.4742362 2023-06-20 11:26:21
Random Forest TensorFlow Regression Tabular 0.6697251 0.4790373 0.5197767 0.8310724 1.5818556 2023-06-21 11:26:21
Random Forest PyTorch Regression Tabular NA 0.8355879 0.9218826 0.9175843 2.8607010 2023-06-22 11:26:21
K-Means Scikit-learn Clustering Time Series 0.5400574 0.8906712 0.7220600 0.5075534 4.0386440 2023-06-23 11:26:21
Random Forest TensorFlow Clustering Tabular 0.9474083 0.5281068 0.8786952 0.8800021 0.7720276 2023-06-24 11:26:21
SVM Keras Clustering Image 0.7737962 0.7035116 0.9888092 0.7316242 2.9454255 2023-06-25 11:26:21
K-Means TensorFlow Clustering Text 0.9086489 0.9044218 0.5018838 0.6379322 2.5769929 2023-06-26 11:26:21
SVM PyTorch Clustering Tabular 0.7261591 0.8396809 0.9727948 0.4790290 0.8059861 2023-06-27 11:26:21
K-Means PyTorch Clustering Image 0.8217888 0.7253423 5.7263733 0.9191775 3.1575926 2023-06-28 11:26:21
Random Forest PyTorch Regression Tabular 0.7632013 0.7542086 0.5698583 0.4943639 1.4408380 2023-06-29 11:26:21
SVM Scikit-learn Clustering Text 0.8657948 0.7050163 0.5382710 5.8587272 NA 2023-06-30 11:26:21
K-Means TensorFlow Clustering Image NA 0.5785291 0.6789853 0.5740273 0.5031344 2023-07-01 11:26:21
Neural Network Scikit-learn Classification Text 0.5301760 0.7390132 0.4079015 NA 2.3519619 2023-07-02 11:26:21
Random Forest Scikit-learn Regression Text 0.6235516 0.8133312 0.6875980 0.8036218 1.6016710 2023-07-03 11:26:21
K-Means PyTorch Regression Image 0.5797723 0.9239937 0.6791934 0.8780088 4.1296679 2023-07-04 11:26:21
Neural Network PyTorch Regression Image 0.9358918 0.7817748 0.8333313 0.5502807 0.3791958 2023-07-05 11:26:21
K-Means Scikit-learn Clustering Image 0.6096070 0.8566729 0.8835550 0.7749245 2.1511756 2023-07-06 11:26:21
Neural Network TensorFlow Clustering Time Series 0.9879326 0.4960430 0.6083089 0.7430476 2.3463140 2023-07-07 11:26:21
Random Forest Keras Clustering Image 0.6684479 0.6769345 0.5116333 0.8996982 3.6525298 2023-07-08 11:26:21
SVM Scikit-learn Classification Image 0.5910590 4.0559897 0.4815344 0.9436522 2.9140091 2023-07-09 11:26:21
Neural Network TensorFlow Clustering Image 0.8948493 0.5480073 0.4362367 0.4072941 3.3693405 2023-07-10 11:26:21
K-Means PyTorch Classification Image 0.8293539 NA NA 0.8044120 3.9075415 2023-07-11 11:26:21
Random Forest PyTorch Regression Tabular 0.7490979 NA 0.5397136 0.4311015 4.3247946 2023-07-12 11:26:21
Neural Network TensorFlow Clustering Time Series 0.7776818 0.4595069 4.8917338 NA 1.6379899 2023-07-13 11:26:21
K-Means Keras Classification Text 0.8596009 0.6408966 0.9764922 0.5725796 2.7363113 2023-07-14 11:26:21
K-Means PyTorch Classification Image NA 0.8800426 0.6903962 0.5840660 4.2165296 2023-07-15 11:26:21
K-Means PyTorch Clustering Tabular 0.9981670 0.5224214 0.5430939 0.6117751 4.9483105 2023-07-16 11:26:21
Neural Network PyTorch Regression Tabular 0.9873966 0.7330510 0.7063273 0.7727755 44.5864462 2023-07-17 11:26:21
Neural Network TensorFlow Regression Tabular 0.8251628 0.8398428 0.3974377 0.6004300 1.9201896 2023-07-18 11:26:21
Neural Network Scikit-learn Regression Text 0.5997712 0.7695913 0.6108306 0.8396194 1.0563159 2023-07-19 11:26:21
SVM PyTorch Classification Image 0.8401141 0.5128148 0.7383640 0.6427164 2.4980234 2023-07-20 11:26:21
Neural Network Scikit-learn Classification Time Series 0.5360992 0.6132307 0.6422285 0.4410119 3.7340431 2023-07-21 11:26:21
Neural Network Keras Classification Time Series 0.5153263 0.8702751 0.5812452 0.8702559 2.5135884 2023-07-22 11:26:21
Neural Network PyTorch Classification Tabular NA 0.7325359 0.9956939 0.5714550 2.4675862 2023-07-23 11:26:21
SVM PyTorch Regression Text 0.7313115 0.4031378 0.9162203 0.6596601 4.2073291 2023-07-24 11:26:21
Neural Network Keras Clustering Text 0.9341363 0.8565945 0.7363842 0.8112663 1.8708785 2023-07-25 11:26:21
K-Means PyTorch Classification Text 0.8635845 0.4211868 0.6985642 0.5994737 4.3129939 2023-07-26 11:26:21
Neural Network Scikit-learn Classification Time Series 0.8713533 0.8474403 0.7344623 0.4339514 20.9334352 2023-07-27 11:26:21
K-Means TensorFlow Regression Tabular 7.1274667 0.5214883 0.4409185 0.6243526 1.7083422 2023-07-28 11:26:21
K-Means Scikit-learn Clustering Image NA 0.9748441 0.5765964 0.9666691 2.3245506 2023-07-29 11:26:21
K-Means TensorFlow Clustering Time Series 0.6855194 6.2076445 0.3276217 0.7850406 3.8359901 2023-07-30 11:26:21
SVM TensorFlow Regression Tabular NA 0.5961590 0.6328822 0.8028875 0.7174099 2023-07-31 11:26:21
SVM Scikit-learn Regression Tabular 5.2005460 0.4893328 0.6801172 0.7793693 1.0624542 2023-08-01 11:26:21
SVM Scikit-learn Clustering Image NA 0.5833625 0.4594248 0.5193953 4.7620796 2023-08-02 11:26:21
Neural Network Scikit-learn Classification Image 0.7893377 0.9259905 0.9748202 0.6510003 0.9599090 2023-08-03 11:26:21
K-Means Scikit-learn Clustering Image 0.7193077 0.9978006 9.3661823 0.8505639 2.8818639 2023-08-04 11:26:21
SVM Keras Regression Text 0.8626288 0.6209857 0.8055003 0.4608237 2.9386409 2023-08-05 11:26:21
SVM PyTorch Classification Image 0.7433345 0.6691664 0.6733706 0.5667117 2.4991599 2023-08-06 11:26:21
Neural Network TensorFlow Classification Tabular 0.9367116 0.8332426 0.9089784 0.5657915 3.2592517 2023-08-07 11:26:21
SVM Scikit-learn Regression Image 0.9503509 0.9317175 0.3914566 0.6592114 1.2261510 2023-08-08 11:26:21
Neural Network Scikit-learn Regression Tabular 0.7108605 0.7558266 0.8533569 0.9882212 2.8080451 2023-08-09 11:26:21
Random Forest Scikit-learn Regression Time Series 0.6384139 0.6349154 0.3873746 0.4405015 1.9236490 2023-08-10 11:26:21
SVM Keras Clustering Text 0.7961752 0.6475731 0.8559475 0.7112206 3.3421696 2023-08-11 11:26:21
Random Forest Scikit-learn Clustering Time Series 0.9561817 0.8173709 0.4930373 0.5076188 0.7919954 2023-08-12 11:26:21
Neural Network Scikit-learn Classification Time Series NA 0.4019310 0.9139634 0.9824059 28.9729934 2023-08-13 11:26:21
K-Means Scikit-learn Regression Tabular 0.8114833 0.7717536 0.9608295 0.4679821 1.0078247 2023-08-14 11:26:21
SVM PyTorch Classification Time Series 0.8157801 0.6132958 0.4041572 0.6421606 NA 2023-08-15 11:26:21
Neural Network Keras Classification Tabular 0.8665565 0.8765184 0.6238729 0.8427310 1.1716780 2023-08-16 11:26:21
K-Means Scikit-learn Classification Image NA 0.4557944 0.9866912 0.8227327 0.9959051 2023-08-17 11:26:21
K-Means TensorFlow Classification Image NA 0.7529214 0.6383852 0.6536372 4.1459837 2023-08-18 11:26:21
Random Forest TensorFlow Regression Tabular 0.9545163 0.6885837 0.9044833 0.6079145 1.4999671 2023-08-19 11:26:21
Neural Network TensorFlow Classification Image 0.5898416 0.7853953 0.7121121 0.6385674 4.6429065 2023-08-20 11:26:21
K-Means Keras Regression Text 0.6187717 0.4389122 0.5627309 0.5585658 4.8526400 2023-08-21 11:26:21
SVM Keras Regression Time Series 0.9856975 NA 0.5000485 0.5231998 2.8991763 2023-08-22 11:26:21
SVM TensorFlow Clustering Text 0.5904885 0.7368908 0.4422562 0.6898238 0.8007055 2023-08-23 11:26:21
Random Forest Scikit-learn Regression Time Series 0.9271925 0.7363961 0.8332587 0.5611203 1.9352864 2023-08-24 11:26:21
K-Means Keras Clustering Image 0.7461389 0.7620926 0.5705784 0.5724770 4.0088881 2023-08-25 11:26:21
Neural Network TensorFlow Regression Text 0.6236155 0.8058808 0.6578928 0.7940536 1.9002146 2023-08-26 11:26:21
SVM TensorFlow Regression Image 0.9353750 0.8829934 0.6446278 0.9811224 0.5263844 2023-08-27 11:26:21
K-Means Scikit-learn Regression Time Series 0.7226526 0.5618924 0.7040953 0.7621823 2.8282461 2023-08-28 11:26:21
K-Means PyTorch Clustering Tabular 0.7574087 0.8950296 0.9059040 0.4461877 4.2410922 2023-08-29 11:26:21
Random Forest Keras Clustering Tabular 0.6796167 0.6989534 0.9865175 0.4453502 NA 2023-08-30 11:26:21
SVM Scikit-learn Classification Text 0.7964754 NA 0.5853089 0.9708539 0.9581034 2023-08-31 11:26:21
SVM TensorFlow Clustering Image 0.5817619 0.4351306 0.8792632 0.5783745 NA 2023-09-01 11:26:21
Neural Network TensorFlow Clustering Image 0.6955408 0.6005430 0.8351695 0.4552402 1.1804709 2023-09-02 11:26:21
SVM Scikit-learn Clustering Text 0.9847062 0.8709382 NA 0.7594268 1.1692169 2023-09-03 11:26:21
Neural Network Keras Clustering Tabular NA 0.8246086 0.9692330 0.7741893 4.3829517 2023-09-04 11:26:21
SVM PyTorch Classification Time Series 0.8283683 0.8731690 0.4403322 0.7891029 1.3233779 2023-09-05 11:26:21
Random Forest PyTorch Clustering Text 0.6625950 0.7103614 0.3764849 0.5604412 1.3899120 2023-09-06 11:26:21
SVM TensorFlow Regression Time Series 0.8867366 0.6641194 0.8977734 0.4090664 0.1032016 2023-09-07 11:26:21
Neural Network Keras Regression Tabular 0.5654368 0.4884715 0.6074049 0.9790092 4.3662783 2023-09-08 11:26:21
Neural Network PyTorch Clustering Image 0.9849105 0.5969157 0.8928782 0.5505358 3.9837146 2023-09-09 11:26:21
Random Forest Keras Clustering Tabular NA 0.6604116 0.9251631 0.8056158 3.1739118 2023-09-10 11:26:21
SVM Keras Regression Text 0.6180252 0.4531603 0.3437203 0.8239779 3.7763022 2023-09-11 11:26:21
SVM TensorFlow Regression Image NA 0.5323672 0.9184253 0.7660045 0.8450380 2023-09-12 11:26:21
Random Forest PyTorch Classification Text 0.5848790 0.7589352 0.6138233 0.5877444 2.3457856 2023-09-13 11:26:21
SVM Scikit-learn Regression Text 0.7598870 0.8413979 NA 0.5626578 1.8209750 2023-09-14 11:26:21
SVM TensorFlow Regression Tabular 0.6685016 0.9990085 0.7386148 0.7586010 0.5595050 2023-09-15 11:26:21
Neural Network PyTorch Clustering Text 0.9144417 0.9598680 0.9484678 NA 2.4820394 2023-09-16 11:26:21
SVM PyTorch Regression Tabular NA 0.7855391 0.3133813 0.9680402 4.6116243 2023-09-17 11:26:21
SVM PyTorch Classification Image 0.6243571 0.6527488 0.6337904 0.4635435 0.2962897 2023-09-18 11:26:21
Random Forest PyTorch Regression Tabular 0.8085725 0.7817064 0.7814054 0.4928972 1.5281585 2023-09-19 11:26:21
Random Forest PyTorch Clustering Image 0.8533886 0.8713910 0.8058949 0.9668418 1.1169506 2023-09-20 11:26:21
K-Means Keras Clustering Tabular 0.5835210 0.4710017 0.7847727 0.8419211 1.2669186 2023-09-21 11:26:21
Neural Network TensorFlow Regression Time Series 0.5838096 0.6459429 0.3941046 0.9297963 4.5513293 2023-09-22 11:26:21
SVM Keras Clustering Time Series 0.5183357 0.9038814 0.5095769 0.5215796 2.3935384 2023-09-23 11:26:21
SVM Scikit-learn Classification Text 0.8682010 0.6302998 0.5511009 0.7525515 2.3848671 2023-09-24 11:26:21
K-Means PyTorch Classification Tabular 0.8319023 0.7431234 0.8631060 0.8206838 3.8268206 2023-09-25 11:26:21
SVM TensorFlow Regression Tabular 0.7373154 0.7526616 0.4951319 0.8080671 0.8570118 2023-09-26 11:26:21
Neural Network PyTorch Regression Image 0.9220852 0.5106858 0.4474935 0.6448910 2.4876002 2023-09-27 11:26:21
K-Means TensorFlow Regression Text 0.9028351 0.6173413 0.9702136 0.4092369 2.2069275 2023-09-28 11:26:21
Neural Network Keras Clustering Tabular 0.7926772 0.6007068 0.3062043 0.7497556 3.0248624 2023-09-29 11:26:21
K-Means TensorFlow Classification Text 0.9341356 0.4157180 0.9984746 0.5518609 4.9978327 2023-09-30 11:26:21
K-Means Scikit-learn Classification Time Series 0.6029206 4.1451506 7.7377491 0.6701525 3.8702996 2023-10-01 11:26:21
Random Forest Scikit-learn Clustering Tabular 0.5559598 0.8990182 NA 0.9745486 2.0495419 2023-10-02 11:26:21
Neural Network TensorFlow Classification Tabular 0.6348748 0.5638425 0.5062336 0.6394212 4.1550100 2023-10-03 11:26:21
SVM TensorFlow Regression Text 0.5285434 0.7108473 0.3100207 0.9038810 0.9364710 2023-10-04 11:26:21
SVM TensorFlow Regression Tabular 0.7655848 0.5792353 0.8165087 5.1312436 0.2492721 2023-10-05 11:26:21
Neural Network TensorFlow Regression Text 0.9683028 0.9644075 0.8839012 0.8034763 1.1017970 2023-10-06 11:26:21
SVM Scikit-learn Classification Tabular 0.5196718 0.5555781 0.8183333 0.9862042 1.7680166 2023-10-07 11:26:21
SVM PyTorch Regression Image 0.5610550 0.6577941 0.3999952 0.4611359 NA 2023-10-08 11:26:21
Neural Network TensorFlow Clustering Text 0.7260995 0.9236382 0.8273995 0.4049920 3.1141328 2023-10-09 11:26:21
K-Means TensorFlow Clustering Tabular 0.9669375 0.9051601 0.8382459 0.6601497 4.5619680 2023-10-10 11:26:21
Neural Network Scikit-learn Classification Time Series 0.6580781 0.5116609 0.7609784 0.4555753 2.5982846 2023-10-11 11:26:21
K-Means Scikit-learn Clustering Tabular 0.7536174 0.8815860 0.8362812 0.8490306 2.5562507 2023-10-12 11:26:21
SVM Keras Clustering Time Series 0.5207864 0.6749121 0.8921450 0.9487292 0.3461907 2023-10-13 11:26:21
SVM TensorFlow Classification Time Series NA 0.6897813 0.7295229 0.6604126 0.2710666 2023-10-14 11:26:21
SVM Keras Regression Image 0.9933151 0.4800880 0.3620233 0.5552270 2.8006838 2023-10-15 11:26:21
Random Forest Scikit-learn Regression Text 0.9825593 0.4483609 0.6413395 0.6606420 2.2470752 2023-10-16 11:26:21
K-Means Scikit-learn Regression Tabular NA 0.8367636 0.3543545 0.8340687 4.2119839 2023-10-17 11:26:21
Random Forest PyTorch Classification Image 0.9759059 0.6978767 0.5852801 0.4054325 0.8873300 2023-10-18 11:26:21
Random Forest TensorFlow Clustering Tabular 0.8195600 0.6621104 NA 0.7536724 0.2223611 2023-10-19 11:26:21
Neural Network Keras Regression Time Series 0.9339591 0.8377049 0.3462069 0.7679750 2.3002900 2023-10-20 11:26:21
Random Forest Scikit-learn Regression Text 0.7273699 0.8593077 0.5441744 0.7826129 1.2636449 2023-10-21 11:26:21
Neural Network Scikit-learn Classification Tabular 0.7577980 0.4953449 0.3776987 0.5452132 0.3431104 2023-10-22 11:26:21
Neural Network Keras Regression Tabular 0.7444233 0.7661351 0.8657646 0.8284316 3.6505316 2023-10-23 11:26:21
Random Forest PyTorch Classification Time Series 0.8334321 0.4812124 0.9633816 NA 0.6468057 2023-10-24 11:26:21
K-Means Scikit-learn Clustering Tabular 0.5698256 0.8508251 0.3506215 0.5195622 3.0807656 2023-10-25 11:26:21
K-Means TensorFlow Classification Tabular 0.5149868 0.7941731 0.9685806 0.9264820 1.4771438 2023-10-26 11:26:21
K-Means Scikit-learn Classification Time Series 0.6539650 0.9739688 0.6658036 0.8432339 0.9501306 2023-10-27 11:26:21
K-Means TensorFlow Classification Image 0.8523404 0.4413748 0.5096960 0.4082473 1.9607825 2023-10-28 11:26:21
K-Means Keras Classification Time Series 0.6009267 NA 0.3538035 0.5490258 4.0270843 2023-10-29 11:26:21
Random Forest TensorFlow Clustering Image 0.8367162 0.5693122 0.6504370 0.5286439 2.0212585 2023-10-30 11:26:21
Random Forest Keras Clustering Text 0.9849560 0.5570234 0.8561609 0.5624838 3.7787713 2023-10-31 11:26:21
SVM TensorFlow Regression Image NA 0.5481873 0.7949605 0.5485359 0.7148829 2023-11-01 11:26:21
K-Means Keras Clustering Image 0.8363011 0.9437527 0.3351582 0.4375158 3.8865800 2023-11-02 11:26:21
Random Forest TensorFlow Clustering Text 0.7218751 0.5497277 0.3510313 0.6753643 1.2609733 2023-11-03 11:26:21
SVM PyTorch Regression Image 0.9340711 0.5631698 0.5820113 0.8396401 3.4192263 2023-11-04 11:26:21
K-Means TensorFlow Clustering Time Series 0.5885749 0.8556390 0.5067033 0.7640390 2.8723296 2023-11-05 11:26:21
Neural Network TensorFlow Classification Image 0.8463130 0.6698439 0.4626690 0.8037230 4.6528091 2023-11-06 11:26:21
SVM PyTorch Classification Text NA 0.8660263 0.4967031 0.4486895 1.9978803 2023-11-07 11:26:21
Random Forest Scikit-learn Regression Time Series 0.9723071 0.4392197 0.8624379 0.9708944 0.4242094 2023-11-08 11:26:21
Neural Network Scikit-learn Regression Text 0.8416240 0.6925427 0.9504596 0.9030951 0.1938619 2023-11-09 11:26:21
Neural Network TensorFlow Regression Text 0.7485874 0.4201682 0.5835719 0.8830542 4.1516069 2023-11-10 11:26:21
Neural Network PyTorch Classification Tabular 0.8089236 0.4375919 0.9342777 0.8937903 2.6712046 2023-11-11 11:26:21
SVM TensorFlow Clustering Tabular 0.9344525 0.9438625 0.5250470 0.9596263 3.8986959 2023-11-12 11:26:21
Random Forest Keras Classification Text 0.7853049 0.4835472 0.6335059 0.7265524 1.2486854 2023-11-13 11:26:21
Neural Network Keras Regression Text 0.5151935 0.7194524 0.4582203 0.5201692 1.7909104 2023-11-14 11:26:21
K-Means TensorFlow Clustering Text 0.9654743 NA 0.7483332 0.7700702 0.2475090 2023-11-15 11:26:21
Neural Network Scikit-learn Clustering Tabular 0.8447634 0.6084060 0.9852868 0.8457289 4.8113124 2023-11-16 11:26:21
Neural Network Scikit-learn Classification Tabular 0.8382567 0.9399000 0.7224452 0.8427504 3.3725527 2023-11-17 11:26:21
SVM TensorFlow Regression Image 0.6078376 0.4130940 0.5504699 0.7128694 4.6727049 2023-11-18 11:26:21
SVM TensorFlow Classification Image 8.2944274 0.7982738 0.7534722 0.4410752 1.4009324 2023-11-19 11:26:21
Random Forest Keras Clustering Text 0.6969322 0.9780367 0.3860445 0.6226678 3.0961717 2023-11-20 11:26:21
K-Means Scikit-learn Classification Time Series 0.8256165 0.7361009 0.9220614 0.9524600 NA 2023-11-21 11:26:21
SVM TensorFlow Classification Time Series 0.5532965 0.9620935 0.6521588 0.7506693 1.6559935 2023-11-22 11:26:21
Neural Network Keras Regression Tabular 0.8289227 0.4313547 0.6145448 0.7229992 4.2557353 2023-11-23 11:26:21
Random Forest Scikit-learn Regression Tabular 0.9997069 0.6512760 0.7101054 0.5613121 4.7410912 2023-11-24 11:26:21
Neural Network PyTorch Clustering Tabular 0.5241060 0.5560947 0.7373487 0.6210694 44.3579008 2023-11-25 11:26:21
Neural Network Keras Classification Text 0.9885871 0.8384926 0.3502431 0.9372077 3.7214282 2023-11-26 11:26:21
SVM Scikit-learn Classification Time Series 0.7034540 0.9887783 0.7778321 0.7998778 1.4595769 2023-11-27 11:26:21
Random Forest Scikit-learn Classification Image 0.9353767 0.5539180 0.4693522 0.8724322 1.4799186 2023-11-28 11:26:21
K-Means Keras Clustering Text 0.8911927 0.7925048 0.7997668 0.6726073 4.8204656 2023-11-29 11:26:21
K-Means PyTorch Clustering Image 0.7835081 0.5188586 0.8757744 0.7781483 0.1503940 2023-11-30 11:26:21
SVM Keras Regression Image 0.8692246 0.7391982 0.8627710 0.5490302 3.6086449 2023-12-01 11:26:21
SVM Scikit-learn Regression Text 0.9392578 0.6783595 NA 0.8232765 3.5606062 2023-12-02 11:26:21
Random Forest TensorFlow Regression Image 0.7020702 0.9832032 0.6641189 0.6565608 3.1511788 2023-12-03 11:26:21
K-Means TensorFlow Classification Time Series 0.6635166 0.7651164 0.4000132 0.6655273 4.9515333 2023-12-04 11:26:21
K-Means Keras Classification Image 0.8337967 0.6097038 0.8427423 0.7895934 1.6282092 2023-12-05 11:26:21
K-Means Scikit-learn Regression Time Series 0.9039230 0.4684575 0.4899866 0.9617684 1.7658621 2023-12-06 11:26:21
Neural Network TensorFlow Classification Time Series 0.8811426 0.4907481 0.6476868 0.4384038 0.4868031 2023-12-07 11:26:21
K-Means Scikit-learn Regression Time Series 0.8989068 0.5351902 0.4989919 0.8948459 2.2697109 2023-12-08 11:26:21
Neural Network Keras Clustering Tabular 0.7177917 0.5505800 0.3936799 0.5754299 13.7913778 2023-12-09 11:26:21
Random Forest TensorFlow Regression Image 0.9089171 0.9103696 0.7406904 0.6663517 1.7825948 2023-12-10 11:26:21
Neural Network Scikit-learn Clustering Text 0.5601045 0.7367337 0.3380324 0.4131480 4.1894018 2023-12-11 11:26:21
Random Forest TensorFlow Classification Text 0.7722445 0.7140345 0.8240517 0.5806276 46.8387412 2023-12-12 11:26:21
K-Means Keras Regression Image NA 0.4688613 0.5223108 0.7015787 1.0100921 2023-12-13 11:26:21
K-Means Scikit-learn Classification Tabular 0.6622929 0.9160838 0.3000943 0.4337056 1.9252938 2023-12-14 11:26:21
Random Forest TensorFlow Clustering Tabular 0.6832308 0.8336886 0.6577904 0.6946574 4.6502898 2023-12-15 11:26:21
SVM Keras Clustering Time Series 0.6980863 0.4406010 NA 0.9562664 0.4028615 2023-12-16 11:26:21
Random Forest TensorFlow Clustering Image NA 0.8247011 0.4933187 0.4632359 0.5525666 2023-12-17 11:26:21
SVM TensorFlow Classification Image 0.6942791 0.7261229 0.7948835 0.8586644 0.8976758 2023-12-18 11:26:21
Neural Network PyTorch Classification Text 0.7243468 0.4490352 3.4388274 0.6458026 3.0165429 2023-12-19 11:26:21
Neural Network PyTorch Classification Image NA 0.6749804 0.8875369 0.7931042 0.8373141 2023-12-20 11:26:21
Neural Network Keras Classification Tabular 0.6866259 NA 0.3026739 0.5561420 4.8435743 2023-12-21 11:26:21
K-Means PyTorch Clustering Image 0.6136348 0.4994647 0.4727767 0.4956954 2.2889012 2023-12-22 11:26:21
Neural Network Keras Clustering Text 0.5365980 9.6741889 0.8186328 0.4962776 2.5574380 2023-12-23 11:26:21
SVM PyTorch Regression Time Series 0.8017243 0.9099852 0.5213891 0.4422952 1.3086272 2023-12-24 11:26:21
Neural Network TensorFlow Regression Text 0.8341064 0.8014134 0.3713247 0.5113880 2.4077052 2023-12-25 11:26:21
Random Forest Keras Classification Text 0.8097452 NA 0.5521637 0.7985316 3.3433768 2023-12-26 11:26:21
Random Forest Keras Clustering Time Series 0.7317470 0.6470593 0.4892753 0.9290144 3.7807680 2023-12-27 11:26:21
K-Means TensorFlow Clustering Text 0.6898929 0.7905841 0.8898983 0.8884754 3.7939536 2023-12-28 11:26:21
Random Forest Keras Clustering Text 0.9316668 0.7272591 0.5193435 NA NA 2023-12-29 11:26:21
SVM Keras Regression Image 0.7595409 0.4373639 0.8522527 0.4662591 4.5313629 2023-12-30 11:26:21
Neural Network PyTorch Regression Image 0.7395909 0.7075016 0.9243105 0.5735125 NA 2023-12-31 11:26:21
K-Means Keras Clustering Time Series 0.5128210 0.8838422 0.6036722 0.5858841 3.8095358 2024-01-01 11:26:21
Neural Network Scikit-learn Clustering Image 0.6706239 0.6755439 0.9369602 5.4997417 0.3724671 2024-01-02 11:26:21
Neural Network TensorFlow Clustering Image NA 0.4311739 0.5641226 0.7090034 0.1337020 2024-01-03 11:26:21
SVM PyTorch Regression Text 0.6994114 0.8717669 0.9748549 0.7213323 1.1409917 2024-01-04 11:26:21
Random Forest PyTorch Clustering Tabular 7.9008618 0.5208183 0.3625027 0.6141316 3.3512656 2024-01-05 11:26:21
Random Forest Scikit-learn Clustering Image 0.7668013 0.5551725 0.7809135 0.6122735 2.1148677 2024-01-06 11:26:21
Neural Network TensorFlow Classification Tabular 0.8039525 0.4988238 0.6456698 0.8971334 2.0717907 2024-01-07 11:26:21
K-Means TensorFlow Classification Image 0.8824416 0.5981290 0.5713542 0.8735757 4.4344758 2024-01-08 11:26:21
Random Forest PyTorch Regression Image 0.9064929 0.8540509 0.7428983 0.5846775 4.4882580 2024-01-09 11:26:21
K-Means Keras Clustering Time Series 0.8590615 0.7116315 0.7927000 0.9482731 4.5549588 2024-01-10 11:26:21
Random Forest PyTorch Clustering Time Series 0.9777618 NA 0.3030543 0.9716890 1.6380026 2024-01-11 11:26:21
K-Means TensorFlow Classification Time Series 0.5091163 0.9266980 0.4168672 0.5960455 3.4861735 2024-01-12 11:26:21
SVM Scikit-learn Regression Tabular 5.9788899 0.9277491 0.7991322 0.6126550 1.4310026 2024-01-13 11:26:21
K-Means PyTorch Classification Text 0.5037814 0.9223471 0.7664698 0.7033805 1.0339882 2024-01-14 11:26:21
SVM TensorFlow Classification Image 0.8237374 5.4327773 0.9762333 0.9646725 1.0046990 2024-01-15 11:26:21
SVM PyTorch Classification Image 9.4901527 0.6707436 0.8327265 0.9257917 NA 2024-01-16 11:26:21
K-Means Keras Classification Image NA 0.9909938 0.9655409 0.4615408 2.2067923 2024-01-17 11:26:21
SVM PyTorch Clustering Tabular 0.9635173 0.8632075 0.7917784 0.6356384 4.1716509 2024-01-18 11:26:21
Neural Network PyTorch Clustering Time Series 0.5301337 0.4163005 0.5086365 0.7320227 0.6874664 2024-01-19 11:26:21
SVM TensorFlow Classification Text 0.9672180 0.4391228 0.3737554 0.7018795 3.7011367 2024-01-20 11:26:21
Random Forest TensorFlow Regression Tabular 0.6758113 0.6783588 0.8472767 0.5163178 2.7041291 2024-01-21 11:26:21
K-Means TensorFlow Regression Time Series 0.5507104 0.9455321 0.7509045 0.9152901 1.5109322 2024-01-22 11:26:21
Neural Network PyTorch Regression Tabular 0.7429359 0.7232211 0.3337302 0.8061645 2.5148160 2024-01-23 11:26:21
K-Means Keras Regression Text 0.6283883 0.6986875 0.5520353 0.9027450 1.5697161 2024-01-24 11:26:21
Random Forest Scikit-learn Classification Time Series 0.6424365 0.4632842 0.9697599 0.9152586 3.0208016 2024-01-25 11:26:21
Random Forest Scikit-learn Clustering Tabular 0.6536450 0.7940681 0.6502808 NA 2.2258352 2024-01-26 11:26:21
Random Forest TensorFlow Regression Text 0.9015129 8.9326190 0.6029104 0.6635264 0.9055871 2024-01-27 11:26:21
SVM TensorFlow Classification Image 0.7695806 0.6282520 0.6203897 0.7663281 0.6712571 2024-01-28 11:26:21
SVM PyTorch Regression Text 0.6556538 0.8653671 0.4462179 0.4962192 2.7788088 2024-01-29 11:26:21
K-Means Scikit-learn Regression Image 0.8051669 0.9786860 0.5580950 0.8041873 4.5218239 2024-01-30 11:26:21
K-Means TensorFlow Classification Tabular 0.8580753 0.5222599 0.5588693 0.5075520 1.7853927 2024-01-31 11:26:21
Neural Network Keras Clustering Time Series 0.6363120 0.7139978 0.3366485 0.8163698 3.6917005 2024-02-01 11:26:21
Neural Network Keras Clustering Time Series 0.7067746 0.5722828 0.8372973 0.5377589 3.3274406 2024-02-02 11:26:21
K-Means TensorFlow Classification Tabular 0.5609430 0.8757127 0.5915531 0.4705308 4.6646191 2024-02-03 11:26:21
SVM TensorFlow Clustering Time Series 0.5905747 0.7465560 0.8755259 0.4991707 4.1228070 2024-02-04 11:26:21
Random Forest Scikit-learn Classification Time Series NA 0.7807495 0.8952436 0.4011953 2.8763658 2024-02-05 11:26:21
K-Means Scikit-learn Classification Image 0.5907192 0.8787485 0.4483958 0.8312438 3.3194494 2024-02-06 11:26:21
Neural Network PyTorch Classification Tabular 0.7625817 0.6375823 0.7601474 0.8394406 4.5020937 2024-02-07 11:26:21
SVM Scikit-learn Clustering Text 0.8545231 0.9490540 0.6305973 0.7089600 2.0576420 2024-02-08 11:26:21
K-Means Keras Classification Image NA 0.7198173 0.9161097 0.4968177 1.7012941 2024-02-09 11:26:21
K-Means Keras Regression Tabular 0.7836561 0.4947729 0.4510185 0.4501326 0.1529743 2024-02-10 11:26:21
SVM TensorFlow Clustering Image 0.6282814 0.8175395 0.7744692 0.4114774 4.1501003 2024-02-11 11:26:21
K-Means Scikit-learn Clustering Text 0.9814634 0.8759568 0.7254265 0.4994489 4.0248054 2024-02-12 11:26:21
SVM PyTorch Clustering Text 0.7417728 NA 0.5067110 0.9345133 0.6118271 2024-02-13 11:26:21
Random Forest Keras Clustering Tabular 0.9029963 0.9143076 0.3956206 0.5449221 2.9266012 2024-02-14 11:26:21
SVM Keras Regression Text 0.7751133 0.9436860 0.7561478 0.6125628 2.3735103 2024-02-15 11:26:21
SVM PyTorch Clustering Image 0.5217063 0.5661427 0.8170182 4.6320729 0.6816746 2024-02-16 11:26:21
SVM PyTorch Clustering Text 0.8165757 0.9901129 0.5209391 0.5334168 4.9047843 2024-02-17 11:26:21
K-Means Keras Classification Image 0.9757017 0.4844269 0.7513828 0.7115347 1.1520098 2024-02-18 11:26:21
K-Means Keras Clustering Image 0.8008059 0.5212094 5.7659164 0.7646516 0.4294475 2024-02-19 11:26:21
SVM PyTorch Regression Text 0.9095944 0.5105349 0.7992448 0.5472114 3.0147522 2024-02-20 11:26:21
K-Means Keras Regression Tabular 0.9421032 0.9363938 0.4394507 0.4346393 3.7204467 2024-02-21 11:26:21
Neural Network TensorFlow Regression Time Series 0.6140399 0.7925755 0.9231505 0.6346197 0.2589729 2024-02-22 11:26:21
Neural Network TensorFlow Regression Text 0.6060224 0.4912626 0.5011867 0.5405220 3.3277164 2024-02-23 11:26:21
K-Means PyTorch Clustering Image 0.8054905 0.6641941 0.5574502 0.5317324 2.7082302 2024-02-24 11:26:21
K-Means Scikit-learn Clustering Image 0.7055142 0.7691788 0.3406644 0.9759177 0.6059985 2024-02-25 11:26:21
SVM PyTorch Regression Text 0.9199307 0.4500785 0.3780585 0.7697803 0.9453670 2024-02-26 11:26:21
Random Forest PyTorch Regression Tabular 0.9500116 0.9294498 0.6611033 0.7341271 2.8830741 2024-02-27 11:26:21
K-Means Scikit-learn Clustering Tabular 0.6767107 0.8821621 0.4873149 NA 1.5521523 2024-02-28 11:26:21
Neural Network Scikit-learn Regression Image 0.6184353 0.7031241 0.8848362 0.6573666 4.6977471 2024-02-29 11:26:21
SVM Scikit-learn Classification Image 0.8902628 0.9802760 0.3102854 0.7245430 4.1118271 2024-03-01 11:26:21
K-Means TensorFlow Regression Tabular 0.6374030 0.6506566 0.5653658 8.1785788 4.9194793 2024-03-02 11:26:21
Neural Network Keras Regression Time Series 0.9113072 0.9904662 0.5361419 0.8212877 1.3723871 2024-03-03 11:26:21
Neural Network TensorFlow Classification Text 0.7118691 0.8007520 0.3135293 0.5030163 4.8516458 2024-03-04 11:26:21
Random Forest Keras Classification Image 0.8337749 0.7808028 0.3870579 0.7000677 2.2130460 2024-03-05 11:26:21
SVM TensorFlow Clustering Image 0.5477677 0.4995729 0.5895499 0.6471749 1.8028421 2024-03-06 11:26:21
SVM Keras Clustering Tabular 0.8119297 0.9291567 0.6450052 0.9223162 0.3467127 2024-03-07 11:26:21
Random Forest TensorFlow Classification Text NA 0.6564938 0.5830028 0.7788514 0.3585498 2024-03-08 11:26:21
Random Forest Scikit-learn Classification Tabular 0.7933042 0.4973400 0.6716564 0.7195024 3.4908552 2024-03-09 11:26:21
SVM PyTorch Clustering Text 0.5840071 4.0756451 0.7165922 0.4692368 2.3438521 2024-03-10 11:26:21
SVM TensorFlow Regression Text 0.8684369 0.7358534 0.3069467 0.7633827 1.2099560 2024-03-11 11:26:21
K-Means Keras Classification Text 0.9313985 0.7164398 0.6248666 0.4703223 3.1084545 2024-03-12 11:26:21
K-Means Scikit-learn Clustering Image 0.6083699 0.8316122 0.9744495 0.6023239 1.3390048 2024-03-13 11:26:21
Random Forest TensorFlow Regression Time Series 0.5478573 0.9341548 0.6633226 0.4857052 2.9303956 2024-03-14 11:26:21
K-Means TensorFlow Classification Tabular 0.5118193 0.4476440 0.7742796 0.8152442 1.8601229 2024-03-15 11:26:21
Neural Network Scikit-learn Regression Time Series 0.8209858 0.8388979 0.5183047 0.5237513 4.1354059 2024-03-16 11:26:21
K-Means TensorFlow Clustering Tabular 0.8035470 0.5124472 0.8417940 0.6351156 4.1214112 2024-03-17 11:26:21
K-Means PyTorch Classification Text 0.7733487 0.9149062 0.8410450 9.3740487 2.4390170 2024-03-18 11:26:21
Neural Network TensorFlow Regression Image 0.6159735 0.8914381 0.6649072 0.5225898 1.8190136 2024-03-19 11:26:21
Random Forest PyTorch Classification Time Series 0.6954530 0.7244763 0.9832097 0.7046830 1.8765430 2024-03-20 11:26:21
Neural Network PyTorch Regression Image 0.7972382 0.8261457 0.3878852 0.6515630 4.0480012 2024-03-21 11:26:21
K-Means Scikit-learn Clustering Tabular 0.7483834 0.5886101 0.3118634 0.4108744 1.7080795 2024-03-22 11:26:21
Random Forest TensorFlow Clustering Image 0.9938928 0.6827007 0.8391107 0.8756549 1.1243909 2024-03-23 11:26:21
K-Means Scikit-learn Classification Image 0.5682199 0.8929821 0.8650089 0.4414275 0.5149729 2024-03-24 11:26:21
Neural Network TensorFlow Regression Time Series NA 0.6755591 0.3841451 0.6845530 2.3867963 2024-03-25 11:26:21
Neural Network TensorFlow Classification Text 0.7021594 0.6146790 4.8590798 0.7364070 2.4624473 2024-03-26 11:26:21
K-Means Keras Classification Image 0.7140998 0.6965275 0.3122867 0.7770559 4.2204527 2024-03-27 11:26:21
SVM TensorFlow Regression Text 0.8587989 0.8969496 0.5053160 0.8131790 1.1840696 2024-03-28 11:26:21
SVM Scikit-learn Regression Tabular 0.8462181 0.6011248 0.8411993 0.5519634 1.9668572 2024-03-29 11:26:21
Neural Network PyTorch Regression Image 0.9956280 0.5042570 0.6625724 0.4059873 4.0611881 2024-03-30 11:26:21
Neural Network TensorFlow Clustering Time Series 0.5641971 0.8272084 5.4366692 0.8340662 4.1357557 2024-03-31 11:26:21
SVM Keras Classification Tabular 0.5520548 0.8955869 0.5602175 0.7213940 1.9845943 2024-04-01 11:26:21
SVM Scikit-learn Classification Tabular 0.8621694 0.4603825 0.3009475 NA 2.3497077 2024-04-02 11:26:21
K-Means TensorFlow Clustering Time Series 0.7891935 0.5439245 0.5098604 0.8928330 1.5861009 2024-04-03 11:26:21
K-Means Scikit-learn Regression Image 0.6370803 0.4851832 0.7525211 NA NA 2024-04-04 11:26:21
SVM Keras Clustering Image 0.5397097 0.6087648 0.9819361 0.6910568 0.6813545 2024-04-05 11:26:21
K-Means Keras Clustering Tabular 0.5428291 0.6702106 0.8929426 0.6001770 4.6753722 2024-04-06 11:26:21
K-Means Scikit-learn Clustering Image 0.9470954 0.8492958 0.3165165 0.8749349 NA 2024-04-07 11:26:21
Random Forest TensorFlow Classification Text 0.5959337 0.7906886 0.9289924 0.6707765 2.7047035 2024-04-08 11:26:21
K-Means PyTorch Clustering Time Series 0.6616858 0.7725571 0.8482389 0.5100653 0.2600294 2024-04-09 11:26:21
Neural Network Keras Classification Text 0.6133282 0.6114250 0.8462633 0.9129844 2.4304213 2024-04-10 11:26:21
K-Means TensorFlow Classification Image 0.6774982 0.9048685 0.6205970 9.2953593 2.0979657 2024-04-11 11:26:21
K-Means TensorFlow Classification Tabular 0.5347119 0.6827723 0.5786037 0.6797859 0.8899722 2024-04-12 11:26:21
SVM PyTorch Regression Time Series 0.7595299 0.9874630 0.5120410 0.4454198 3.3164071 2024-04-13 11:26:21
Neural Network Keras Classification Image 0.5338063 0.7804853 0.3459864 0.6326957 4.8586394 2024-04-14 11:26:21
K-Means Keras Classification Image 0.9001783 0.4757588 0.4597648 NA 2.8547252 2024-04-15 11:26:21
K-Means TensorFlow Regression Tabular 0.6168560 0.8057065 0.4726225 0.9410644 3.6022911 2024-04-16 11:26:21
Random Forest TensorFlow Classification Tabular 0.7700060 0.5950624 0.6388539 0.5220836 4.3540722 2024-04-17 11:26:21
K-Means PyTorch Clustering Text 0.9400395 0.8117963 0.8232111 0.4401842 2.1465683 2024-04-18 11:26:21
K-Means PyTorch Clustering Time Series 0.8254387 0.4417847 0.6316669 0.9264103 0.6701566 2024-04-19 11:26:21
Random Forest Keras Classification Time Series 0.7664789 NA 0.3404911 0.6336430 3.1026996 2024-04-20 11:26:21
SVM Scikit-learn Regression Tabular 0.6621669 0.9134424 0.9704529 0.7250566 46.9856258 2024-04-21 11:26:21
K-Means Keras Clustering Text 0.6665010 0.5363077 0.9599070 0.9808395 3.3427109 2024-04-22 11:26:21
Random Forest TensorFlow Regression Time Series 0.8347435 0.9022247 0.8496327 0.4399388 0.4763349 2024-04-23 11:26:21
Neural Network Keras Regression Tabular 0.9970697 0.5675657 0.9939295 0.7889908 1.8378644 2024-04-24 11:26:21
SVM Scikit-learn Clustering Image NA 0.7857291 0.6811376 0.4444633 2.7982506 2024-04-25 11:26:21
Neural Network PyTorch Regression Time Series 0.7788917 0.8164903 0.9739378 0.6252814 2.0757180 2024-04-26 11:26:21
Neural Network Scikit-learn Clustering Text 0.8653253 0.7075929 0.3529235 0.8822887 4.1848557 2024-04-27 11:26:21
Random Forest Scikit-learn Regression Text 0.7326028 0.5831864 0.5559765 0.6600833 4.0918879 2024-04-28 11:26:21
K-Means Scikit-learn Clustering Text 0.5300712 NA 0.4577669 0.9983094 3.0979720 2024-04-29 11:26:21
Random Forest PyTorch Classification Image 0.7811484 0.4199136 0.4370832 0.7354356 1.9299439 2024-04-30 11:26:21
Random Forest Scikit-learn Clustering Time Series 0.9788126 0.5823678 0.3985621 0.5926973 1.3511482 2024-05-01 11:26:21
Random Forest Scikit-learn Clustering Time Series 0.5876515 0.7918977 0.7356895 5.3206680 0.6187842 2024-05-02 11:26:21
Random Forest Scikit-learn Clustering Image NA 0.9629829 0.8469253 0.6103123 1.8389986 2024-05-03 11:26:21
SVM TensorFlow Clustering Time Series 0.6004668 0.9227227 0.7048089 0.6235200 2.1245453 2024-05-04 11:26:21
K-Means Scikit-learn Regression Image 0.7679138 0.8596389 0.4028739 0.4412282 3.4115323 2024-05-05 11:26:21
Neural Network PyTorch Classification Image 0.5483382 0.8730684 0.8677866 0.6217445 3.3249831 2024-05-06 11:26:21
Neural Network Scikit-learn Clustering Image NA 0.7989909 0.7451683 0.6785431 0.4435299 2024-05-07 11:26:21
K-Means Scikit-learn Regression Text 0.8780817 0.5561721 0.5717160 NA 2.0367706 2024-05-08 11:26:21
Neural Network PyTorch Classification Time Series 0.6737858 0.9443170 0.7718912 0.7940377 0.9907164 2024-05-09 11:26:21
K-Means Scikit-learn Classification Image 0.8324559 0.8024394 0.4819335 0.8252594 0.8683823 2024-05-10 11:26:21
Neural Network PyTorch Classification Time Series 0.8977250 0.7362644 0.5416345 0.4050182 4.1537805 2024-05-11 11:26:21
Random Forest Scikit-learn Classification Text 0.9635889 0.4665937 0.9422401 0.5306004 0.3053846 2024-05-12 11:26:21
Neural Network PyTorch Clustering Image 0.6173210 0.6682333 0.5033532 0.7970106 2.1539961 2024-05-13 11:26:21
K-Means Scikit-learn Clustering Tabular 0.6996580 0.6762150 0.6289918 0.6903936 0.9241979 2024-05-14 11:26:21
K-Means Scikit-learn Regression Text 0.5762080 0.9187382 0.9230988 0.4031881 4.4087181 2024-05-15 11:26:21
K-Means TensorFlow Regression Tabular 0.9962418 0.7279889 0.7954049 0.8826968 28.2949852 2024-05-16 11:26:21
K-Means TensorFlow Regression Time Series 0.9635005 0.6282403 0.3435423 0.8636857 1.2337651 2024-05-17 11:26:21
K-Means Scikit-learn Classification Time Series 0.7699786 0.9860802 0.4031121 0.7290515 2.5618937 2024-05-18 11:26:21
SVM Scikit-learn Classification Text 0.9210166 NA 0.3054891 0.4398780 3.6842832 2024-05-19 11:26:21
SVM Keras Clustering Time Series 0.7604790 0.6535291 0.7416453 0.8599090 4.7947782 2024-05-20 11:26:21
Neural Network PyTorch Classification Tabular NA 0.4252148 0.6133714 0.7502282 1.1801854 2024-05-21 11:26:21
K-Means Keras Classification Image 0.5445622 0.8439425 0.3939924 0.8691760 4.4422907 2024-05-22 11:26:21
Neural Network TensorFlow Classification Image 0.8776352 0.9508459 0.9705539 0.8510482 4.6780902 2024-05-23 11:26:21
K-Means TensorFlow Regression Image 0.5638567 NA 0.6707618 NA 4.5904607 2024-05-24 11:26:21
K-Means TensorFlow Regression Text 0.9130338 0.9150050 0.4693253 0.7108048 3.2135303 2024-05-25 11:26:21
SVM TensorFlow Clustering Time Series 0.8910140 0.5753309 0.6504225 0.4841947 3.1861212 2024-05-26 11:26:21
SVM Scikit-learn Classification Tabular 0.8543723 0.9464621 0.7757342 0.8026945 2.0757321 2024-05-27 11:26:21
Random Forest TensorFlow Classification Time Series 0.5180802 0.8523771 0.3533675 0.7722843 3.7867496 2024-05-28 11:26:21
SVM Scikit-learn Clustering Time Series 0.6515642 0.8829441 0.4922927 0.8453519 2.7033407 2024-05-29 11:26:21
Random Forest Scikit-learn Classification Time Series 0.6315563 0.4108039 0.8648758 0.5019286 3.4194465 2024-05-30 11:26:21
Random Forest TensorFlow Classification Image 0.6800682 0.9776862 0.6217654 0.5169851 2.1994437 2024-05-31 11:26:21
Random Forest TensorFlow Regression Tabular 0.5438214 0.8360026 0.6826040 0.9342457 3.6843327 2024-06-01 11:26:21
Random Forest PyTorch Clustering Tabular 0.9684789 0.5828507 0.6029712 NA 4.1399104 2024-06-02 11:26:21
Random Forest Keras Clustering Time Series 0.7769011 0.8976368 0.3307300 0.9449371 0.8175377 2024-06-03 11:26:21
Neural Network TensorFlow Clustering Image 0.6527622 0.5689127 0.4160248 0.8552292 4.1813556 2024-06-04 11:26:21
SVM PyTorch Clustering Image 0.6984908 0.9236523 0.6118791 0.7582932 2.7474530 2024-06-05 11:26:21
Random Forest PyTorch Regression Tabular 0.7236013 0.4675482 0.4464298 0.7924796 4.2388630 2024-06-06 11:26:21
K-Means Keras Regression Image 0.8002972 0.8222116 0.3349853 0.9334768 2.2133784 2024-06-07 11:26:21
SVM Scikit-learn Classification Image 0.7578397 0.7244191 0.8905548 0.7472841 1.9572984 2024-06-08 11:26:21
SVM TensorFlow Classification Image 0.9596960 0.4579207 0.9868348 0.7794348 4.5837976 2024-06-09 11:26:21
Random Forest Keras Classification Text 0.7484817 0.5451363 0.8552000 0.4938860 1.3307294 2024-06-10 11:26:21
Neural Network TensorFlow Classification Time Series 0.9960790 0.4074424 0.8976494 0.6843527 4.2366404 2024-06-11 11:26:21
Random Forest PyTorch Regression Tabular 0.9257125 0.6812608 0.4693840 0.8298383 2.4717008 2024-06-12 11:26:21
Neural Network PyTorch Clustering Tabular 0.6042553 0.5807592 0.9724389 0.5625656 2.6190677 2024-06-13 11:26:21
K-Means Scikit-learn Regression Text 0.9652976 0.7590145 0.4378480 0.5213525 1.6114946 2024-06-14 11:26:21
SVM PyTorch Classification Time Series 0.5581832 0.5783427 0.9660009 0.5882961 2.9079068 2024-06-15 11:26:21
K-Means PyTorch Regression Tabular 0.9087249 0.5799515 0.9963735 0.5449004 1.6935349 2024-06-16 11:26:21
Neural Network Keras Clustering Image 0.6903116 0.8459159 0.7982060 0.5289476 0.2924201 2024-06-17 11:26:21
Neural Network TensorFlow Clustering Tabular 0.9389872 0.4288857 0.9868006 0.6549105 1.4327932 2024-06-18 11:26:21
K-Means TensorFlow Regression Image 0.9340283 0.9417370 0.6986778 0.9447031 0.1312573 2024-06-19 11:26:21
Neural Network Keras Regression Text NA 0.9113583 0.4816792 0.7042358 4.8939385 2024-06-20 11:26:21
K-Means Scikit-learn Regression Image 0.8950152 0.8006828 0.6058971 0.5127522 4.8321613 2024-06-21 11:26:21
SVM TensorFlow Classification Tabular 0.6523396 0.7559329 0.7154927 0.4461830 2.0362610 2024-06-22 11:26:21
K-Means PyTorch Regression Tabular 0.5404596 0.9353815 0.3511571 0.8176937 3.6690172 2024-06-23 11:26:21
SVM TensorFlow Regression Tabular 0.7014901 0.5111981 0.7356403 0.6296793 1.7944499 2024-06-24 11:26:21
K-Means PyTorch Regression Text 0.5867623 0.4473815 0.9868245 0.8930986 3.3883565 2024-06-25 11:26:21
Neural Network Scikit-learn Clustering Text 0.8474755 0.5437061 0.4330754 0.7957065 4.0466072 2024-06-26 11:26:21
K-Means Keras Clustering Time Series 0.6730499 0.8767470 0.8548166 0.8777449 4.7391056 2024-06-27 11:26:21
Neural Network TensorFlow Classification Tabular 0.9878051 0.4208022 0.9355292 0.5631676 2.0610402 2024-06-28 11:26:21
K-Means Keras Classification Image 0.8204860 0.7496841 0.9605911 0.8154154 3.9381601 2024-06-29 11:26:21
K-Means TensorFlow Clustering Text 0.9112403 0.9972625 0.9720949 0.5584372 1.4030494 2024-06-30 11:26:21
Random Forest PyTorch Clustering Tabular 0.5662623 0.9134177 0.6650217 0.9634411 NA 2024-07-01 11:26:21
Neural Network Keras Clustering Tabular 0.9310072 NA 0.9841155 0.7818246 NA 2024-07-02 11:26:21
Random Forest TensorFlow Regression Text 0.9613786 0.4381845 0.8301171 0.5947071 3.0572912 2024-07-03 11:26:21
Neural Network PyTorch Regression Time Series 0.7435310 0.8988241 0.4131700 0.5617071 3.3315793 2024-07-04 11:26:21
Random Forest Keras Clustering Text 0.8031265 0.7593871 0.6338301 0.5145560 3.4714232 2024-07-05 11:26:21
SVM Keras Regression Text 0.8824049 0.4689598 0.8028318 0.8167846 0.6898991 2024-07-06 11:26:21
K-Means TensorFlow Regression Image 0.5874193 0.4563144 0.4731382 0.5312294 4.6987959 2024-07-07 11:26:21
Neural Network TensorFlow Classification Image 0.7512830 0.9457761 0.7484306 0.7571820 0.9877981 2024-07-08 11:26:21
Neural Network TensorFlow Clustering Time Series 0.6993315 0.8015202 0.7666203 0.5587810 3.1514917 2024-07-09 11:26:21
K-Means PyTorch Clustering Tabular 0.5731870 0.8975721 0.4138898 0.7971814 1.1911128 2024-07-10 11:26:21
Neural Network TensorFlow Regression Text 0.6837672 0.9273873 0.6955597 0.8889638 1.6053604 2024-07-11 11:26:21
Neural Network Keras Clustering Time Series 0.5340862 0.7430634 0.8401388 0.8668151 2.7776469 2024-07-12 11:26:21
Random Forest Keras Classification Image 0.5129060 0.7104678 NA 0.8565108 2.1442478 2024-07-13 11:26:21
Neural Network TensorFlow Classification Text 0.5675831 0.6582564 0.3084722 0.5126334 0.8851054 2024-07-14 11:26:21
SVM TensorFlow Classification Tabular 0.9815576 0.5901680 0.3063269 0.4530310 0.9375884 2024-07-15 11:26:21
SVM Scikit-learn Clustering Image 0.7747648 0.6607576 0.5499205 0.8193693 2.1489095 2024-07-16 11:26:21
K-Means Keras Clustering Image 0.9829111 0.8643278 0.9483358 0.6210083 3.8106866 2024-07-17 11:26:21
Neural Network PyTorch Clustering Image 0.7162489 0.7611541 0.4600737 0.6594077 4.5000268 2024-07-18 11:26:21
K-Means Scikit-learn Clustering Time Series 0.6559081 0.9355140 0.7440546 0.4186895 0.5122255 2024-07-19 11:26:21
SVM TensorFlow Clustering Tabular 0.7530709 0.6660280 0.4554531 0.5557459 2.0262091 2024-07-20 11:26:21
Neural Network TensorFlow Classification Text 0.7197558 0.7642537 0.5251690 0.4202058 0.5912033 2024-07-21 11:26:21
Neural Network Keras Regression Time Series 0.5528323 0.7787845 0.8936295 0.9275115 0.1813032 2024-07-22 11:26:21
K-Means TensorFlow Classification Time Series 0.8204132 0.7550183 0.8102030 0.5460380 3.3424215 2024-07-23 11:26:21
SVM PyTorch Clustering Time Series 0.6080191 0.8215803 0.3667795 0.7344023 3.0520023 2024-07-24 11:26:21
Neural Network Keras Classification Image 0.8097940 0.5424601 0.6000914 0.4233876 0.8987937 2024-07-25 11:26:21
K-Means TensorFlow Clustering Tabular 0.8251005 0.7074183 0.3204188 NA 1.2456829 2024-07-26 11:26:21
SVM Keras Clustering Time Series 0.5760124 0.4625349 0.6366231 0.5938164 0.2161680 2024-07-27 11:26:21
K-Means TensorFlow Classification Text 0.5306748 0.6307068 0.7637038 0.9387515 4.1912413 2024-07-28 11:26:21
Random Forest TensorFlow Clustering Tabular 0.8903808 0.6926002 0.3829518 0.9328709 4.8759839 2024-07-29 11:26:21
Neural Network TensorFlow Regression Text 0.7299002 0.7913346 0.5022195 0.5951744 0.7637517 2024-07-30 11:26:21
Random Forest PyTorch Clustering Tabular 0.5290819 0.9703186 0.5784856 0.9405765 1.2342285 2024-07-31 11:26:21
SVM PyTorch Regression Text 0.9974332 0.7603906 0.9436717 0.9976946 4.3552067 2024-08-01 11:26:21
Random Forest Keras Regression Image 0.5288903 0.8461563 0.9952785 0.8952494 4.6396726 2024-08-02 11:26:21
Random Forest TensorFlow Clustering Time Series 0.8475176 0.7037596 0.3314379 0.9069228 2.1561742 2024-08-03 11:26:21
SVM TensorFlow Classification Text 0.9918395 0.7804624 0.8327055 0.5494052 0.3480501 2024-08-04 11:26:21
K-Means PyTorch Regression Tabular 0.6195901 0.4425593 0.5602068 0.7460214 0.2930200 2024-08-05 11:26:21
Random Forest TensorFlow Classification Time Series 0.5711247 0.5526349 NA 0.4403535 2.9123543 2024-08-06 11:26:21
K-Means PyTorch Regression Time Series 0.5606925 0.6171119 0.8279855 0.4569508 20.2518603 2024-08-07 11:26:21
Random Forest TensorFlow Clustering Tabular 0.6516376 0.6834960 0.9428985 0.9993356 0.2403969 2024-08-08 11:26:21
K-Means Keras Clustering Text 0.5505229 0.4273892 0.9656500 0.5959835 2.9579367 2024-08-09 11:26:21
SVM TensorFlow Regression Image 0.8460807 0.4840145 0.7039858 NA 0.1553824 2024-08-10 11:26:21
Random Forest TensorFlow Clustering Image 0.5311459 0.5660886 5.4998481 0.8839990 3.9581226 2024-08-11 11:26:21
SVM Scikit-learn Regression Text 0.7547111 0.9829196 0.8512844 0.9148140 1.6015208 2024-08-12 11:26:21
Random Forest PyTorch Clustering Time Series 0.9983484 0.5988082 0.4757010 0.9985770 0.2972642 2024-08-13 11:26:21
SVM TensorFlow Classification Time Series 0.9069851 0.6892246 0.6948518 0.5448979 2.9829259 2024-08-14 11:26:21
SVM TensorFlow Clustering Time Series 0.8076097 0.5176586 NA 0.4242105 2.0486536 2024-08-15 11:26:21
Random Forest Scikit-learn Classification Time Series 0.6531268 NA 0.7596418 0.6467149 4.8716220 2024-08-16 11:26:21
Random Forest Scikit-learn Clustering Text 0.8119479 0.5684099 0.4682792 0.4780484 2.7665734 2024-08-17 11:26:21
Random Forest PyTorch Classification Time Series 0.7635207 0.5241955 0.4341151 0.4134555 1.4486720 2024-08-18 11:26:21
Neural Network Scikit-learn Clustering Tabular 0.7130417 0.7099436 0.9427674 0.6162561 3.5762504 2024-08-19 11:26:21
K-Means Keras Classification Time Series 0.5653552 0.4033035 0.3712626 0.8702430 1.4303108 2024-08-20 11:26:21
Neural Network Scikit-learn Regression Image 0.9433021 0.4045984 0.6541705 0.7397084 4.5307921 2024-08-21 11:26:21
Neural Network Keras Regression Time Series NA 0.5314413 0.4545969 0.5876668 1.9361377 2024-08-22 11:26:21
SVM PyTorch Classification Text 0.5973113 0.4220328 0.3272510 0.7926052 2.7943560 2024-08-23 11:26:21
Random Forest PyTorch Clustering Image 0.6838797 0.4648155 0.3252132 0.5392109 0.3478082 2024-08-24 11:26:21
K-Means PyTorch Classification Text 0.7070649 0.6033164 0.4226552 0.4086289 2.1879941 2024-08-25 11:26:21
SVM PyTorch Clustering Text 0.9137689 0.8815514 0.9067392 0.8586120 NA 2024-08-26 11:26:21
Neural Network PyTorch Regression Image 0.8668072 0.7432292 0.4977351 0.7742459 4.0476791 2024-08-27 11:26:21
Random Forest Keras Clustering Text 0.8846524 NA 0.9653214 0.8573816 1.1991609 2024-08-28 11:26:21
SVM Keras Regression Text 0.5055156 5.7609329 0.7071356 0.4233628 1.2077874 2024-08-29 11:26:21
Random Forest PyTorch Clustering Time Series 0.7080770 0.9590522 0.6056299 0.9022718 4.1047960 2024-08-30 11:26:21
SVM Scikit-learn Clustering Tabular 0.7406721 0.6382090 0.7060622 0.7717157 4.6589523 2024-08-31 11:26:21
Random Forest TensorFlow Clustering Text 0.5095961 0.4522557 0.6616889 0.7380372 0.5672685 2024-09-01 11:26:21
Neural Network PyTorch Classification Text 0.6299066 0.7702399 0.8311434 7.7476843 2.3052873 2024-09-02 11:26:21
K-Means Scikit-learn Classification Text 0.8801449 0.4683030 0.4977472 NA 1.7535260 2024-09-03 11:26:21
Neural Network TensorFlow Regression Time Series 0.5685549 0.6071339 0.5471353 0.7521619 4.3663734 2024-09-04 11:26:21
K-Means PyTorch Regression Tabular 0.7676551 7.0444716 0.9258660 0.7485702 0.5092721 2024-09-05 11:26:21
Random Forest PyTorch Classification Image 0.6076009 0.9245335 0.9625196 0.9944075 1.1345171 2024-09-06 11:26:21
SVM Keras Regression Time Series NA 0.6961279 0.9247908 0.8540381 3.7870949 2024-09-07 11:26:21
Neural Network TensorFlow Classification Time Series 0.6206007 0.8213553 0.5936136 0.6653763 0.3513399 2024-09-08 11:26:21
K-Means TensorFlow Clustering Tabular 0.9879369 0.9956901 0.8462559 0.8244382 2.5134234 2024-09-09 11:26:21
Neural Network Scikit-learn Regression Tabular 0.9007686 0.4788935 NA 0.6335383 2.2663245 2024-09-10 11:26:21
K-Means Keras Regression Time Series 0.9797883 0.5648389 0.6482779 0.5373253 1.7385658 2024-09-11 11:26:21
Neural Network Keras Classification Image 0.7439270 0.6367456 0.4432761 0.7581111 2.0334043 2024-09-12 11:26:21
K-Means Keras Regression Time Series 0.5548681 0.6530969 0.7137912 0.9569093 2.6967089 2024-09-13 11:26:21
K-Means Keras Regression Tabular 0.7739797 0.6466126 0.4302951 0.9574837 0.8907001 2024-09-14 11:26:21
Random Forest TensorFlow Clustering Image 0.7271887 NA 0.5320953 0.6051432 2.9027798 2024-09-15 11:26:21
K-Means TensorFlow Regression Tabular 0.9221785 0.8284196 0.8985211 0.7166065 4.0466184 2024-09-16 11:26:21
Random Forest PyTorch Clustering Image 0.5490413 0.7647431 0.4449531 0.5269815 3.8247886 2024-09-17 11:26:21

Datos generales de la base de datos

names(datos)
##  [1] "Algorithm"     "Framework"     "Problem_Type"  "Dataset_Type" 
##  [5] "Accuracy"      "Precision"     "Recall"        "F1_Score"     
##  [9] "Training_Time" "Date"
dim(datos)
## [1] 560  10

Las variables involucradas en este estudio son:

  1. Algorithm (categórica): Tipo de algoritmo de IA utilizado(‘Neural Network’, ‘Random Forest’, ‘SVM’, ‘K-Means’)

  2. Accuracy (numérica, continua): Precisión del modelo en el conjunto de prueba (entre 0 y 1).

  3. Framework (categórica): Framework o biblioteca utilizada para la implementación del modelo de IA(‘TensorFlow’, ‘PyTorch’, ‘Keras’,‘Scikit-learn’)

  4. Problem_Type (categórica): Tipo de problema abordado por el modelo.(‘Classification’, ‘Regression’, ‘Clustering’)

  5. Dataset_Type (categórica): Tipo de datos utilizados en el entrenamiento del modelo(‘Image’, ‘Text’, ‘Tabular’, ‘Time Series’.)

  6. Accuracy (numérica, continua): Precisión del modelo en el conjunto de prueba (entre 0 y 1).}

  7. Precision (numérica, continua): Precisión del modelo (valor entre 0 y 1).

  8. Recall (numérica, continua): Sensibilidad o capacidad del modelo para identificar correctamente los positivos (entre 0 y 1).

  9. F1_Score (numérica, continua): Medida armónica entre precisión y recall (entre 0 y 1).

  10. Training_Time (numérica, continua): Tiempo de entrenamiento del modelo en horas.

  11. Date (fecha): Fecha en la que se realizó la evaluación del modelo, cubriendo el último año.

Cuadro de operacionalización de variable

Variable Definición Conceptual Definición Operacional Indicador(es) Escala de Medición
Accuracy La precisión de un modelo es la proporción de predicciones correctas sobre el total de predicciones realizadas. Medida numérica que representa la precisión del modelo en un conjunto de prueba. Valor numérico entre 0 y 1 Continua, entre 0 y 1
Algorithm Tipo de algoritmo de Inteligencia Artificial utilizado en el modelo. Categorización de los algoritmos usados (Ej. ‘Neural Network’, ‘Random Forest’, etc.). Algoritmos listados: Neural Network, Random Forest, SVM, K-Means Categórica
Framework Herramienta o biblioteca usada para implementar el modelo de IA. Nombre del framework que se utilizó para entrenar el modelo. Frameworks listados: TensorFlow, PyTorch, Keras, Scikit-learn Categórica
Problem_Type Tipo de problema que el modelo está resolviendo, como clasificación, regresión, etc. Categorización del tipo de problema abordado en el modelo. Clasificación, Regresión, Clustering Categórica
Dataset_Type Tipo de datos utilizados en el entrenamiento del modelo (ej. Imagen, Texto, Tabular, Series Temporales). Categorización de los datos usados en el entrenamiento del modelo. Tipos de datos: Imagen, Texto, Tabular, Series Temporales Categórica
Precision La precisión es la proporción de verdaderos positivos entre todos los positivos predichos. Medida numérica de la precisión del modelo en cuanto a las predicciones positivas. Valor numérico entre 0 y 1 Continua, entre 0 y 1
Recall La sensibilidad o recall mide la proporción de verdaderos positivos entre todos los casos realmente positivos. Medida numérica de la capacidad del modelo para identificar correctamente los positivos. Valor numérico entre 0 y 1 Continua, entre 0 y 1
F1_Score El F1-Score es la medida armónica entre precisión y recall, que balancea ambos aspectos. Medida numérica que refleja el equilibrio entre la precisión y el recall del modelo. Valor numérico entre 0 y 1 Continua, entre 0 y 1
Training_Time Tiempo total que se toma para entrenar el modelo, generalmente expresado en horas. Medida numérica del tiempo en horas utilizado para entrenar el modelo. Valor numérico que representa el tiempo (en horas) Continua, en horas
Date Fecha en la que se realizó la evaluación del modelo, cubriendo el último año. Fecha en que se realizó la evaluación del modelo. Fecha de evaluación Fecha

Tratamiento de NA

Vistas las variables, es necesario determinar si existen valores NA que puedan complicar la aplicacion de funciones o interpretacion de la base de datos, un rápido diagnostico que nos permite saber los valores NA y donde pueden ser encontrados, puede realizarse a través del ‘Missingness Map’ o Mapa de valores faltantes:

suppressWarnings(missmap(datos))

Se encontraron datos faltantes en las variables numericas “Precision”, “Recall”, “F1_Score”, “Training_Time” y “Accuracy”, la cantidad de valores NA por variable se muestra a continuacion:

na_colum <- colSums(is.na(datos))
print(na_colum)
##     Algorithm     Framework  Problem_Type  Dataset_Type      Accuracy 
##             0             0             0             0            39 
##     Precision        Recall      F1_Score Training_Time          Date 
##            19            20            20            20             0

El tratamiento de los datos faltantes se realizara através imputación de mediana para asi evitar sesgos a la hora de comparar las variables:

#Tratamiento de los NA
conversion_atp <- function(x) {
  iqr <- IQR(x, na.rm = TRUE)
  cuartil1 <- quantile(x, 0.25, na.rm = TRUE)
  cuartil3 <- quantile(x, 0.75, na.rm = TRUE)
  lim_inferior <- cuartil1 - 1.5 * iqr
  lim_superior <- cuartil3 + 1.5 * iqr
  x <- ifelse(x < lim_inferior | x > lim_superior, NA, x)
  return(x)
}
datos$Accuracy <- conversion_atp(datos$Accuracy)
datos$Precision <- conversion_atp(datos$Precision)
datos$Recall <- conversion_atp(datos$Recall)
datos$F1_Score <- conversion_atp(datos$F1_Score)
datos$Training_Time <- conversion_atp(datos$Training_Time)

tratamiento_atp <- function(x) {
  x[is.na(x)] <- median(x, na.rm = TRUE)
  return(x)
}
datos$Accuracy <- tratamiento_atp(datos$Accuracy)
datos$Precision <- tratamiento_atp(datos$Precision)
datos$Recall <- tratamiento_atp(datos$Recall)
datos$F1_Score <- tratamiento_atp(datos$F1_Score)
datos$Training_Time <- tratamiento_atp(datos$Training_Time)
#K-Means
datos_K <- datos %>%
  filter(Problem_Type == "Regression", Algorithm == "K-Means")

summary(datos_K)
##   Algorithm          Framework         Problem_Type       Dataset_Type      
##  Length:50          Length:50          Length:50          Length:50         
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##     Accuracy        Precision          Recall          F1_Score     
##  Min.   :0.5405   Min.   :0.4231   Min.   :0.3193   Min.   :0.4032  
##  1st Qu.:0.6218   1st Qu.:0.5249   1st Qu.:0.4556   1st Qu.:0.5328  
##  Median :0.7678   Median :0.6486   Median :0.5685   Median :0.7313  
##  Mean   :0.7573   Mean   :0.6709   Mean   :0.6172   Mean   :0.7114  
##  3rd Qu.:0.8979   3rd Qu.:0.8181   3rd Qu.:0.7521   3rd Qu.:0.8815  
##  Max.   :0.9962   Max.   :0.9787   Max.   :0.9964   Max.   :0.9618  
##  Training_Time         Date                       
##  Min.   :0.1313   Min.   :2023-03-13 11:26:21.07  
##  1st Qu.:1.5249   1st Qu.:2023-08-16 05:26:21.07  
##  Median :2.3868   Median :2024-02-26 11:26:21.07  
##  Mean   :2.5702   Mean   :2024-01-22 10:28:45.07  
##  3rd Qu.:3.7187   3rd Qu.:2024-06-15 23:26:21.07  
##  Max.   :4.9195   Max.   :2024-09-16 11:26:21.07
##   Algorithm          Framework         Problem_Type       Dataset_Type      
##  Length:49          Length:49          Length:49          Length:49         
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##     Accuracy        Precision          Recall          F1_Score     
##  Min.   :0.5152   Min.   :0.4046   Min.   :0.3082   Min.   :0.4060  
##  1st Qu.:0.6236   1st Qu.:0.5676   1st Qu.:0.4475   1st Qu.:0.5952  
##  Median :0.7531   Median :0.7232   Median :0.5836   Median :0.7042  
##  Mean   :0.7729   Mean   :0.7034   Mean   :0.6048   Mean   :0.7115  
##  3rd Qu.:0.9113   3rd Qu.:0.8059   3rd Qu.:0.7075   3rd Qu.:0.8213  
##  Max.   :0.9986   Max.   :0.9905   Max.   :0.9939   Max.   :0.9882  
##  Training_Time         Date                       
##  Min.   :0.1813   Min.   :2023-03-14 11:26:21.07  
##  1st Qu.:1.8190   1st Qu.:2023-07-19 11:26:21.07  
##  Median :2.3868   Median :2023-11-23 11:26:21.07  
##  Mean   :2.5956   Mean   :2023-12-13 12:54:30.88  
##  3rd Qu.:4.0477   3rd Qu.:2024-03-30 11:26:21.07  
##  Max.   :4.8939   Max.   :2024-09-10 11:26:21.07
##   Algorithm          Framework         Problem_Type       Dataset_Type      
##  Length:38          Length:38          Length:38          Length:38         
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##     Accuracy        Precision          Recall          F1_Score     
##  Min.   :0.5198   Min.   :0.4382   Min.   :0.3163   Min.   :0.4311  
##  1st Qu.:0.6824   1st Qu.:0.6791   1st Qu.:0.5594   1st Qu.:0.5460  
##  Median :0.7531   Median :0.8035   Median :0.6851   Median :0.6604  
##  Mean   :0.7827   Mean   :0.7598   Mean   :0.6900   Mean   :0.6765  
##  3rd Qu.:0.9268   3rd Qu.:0.8801   3rd Qu.:0.8325   3rd Qu.:0.8019  
##  Max.   :0.9997   Max.   :0.9832   Max.   :0.9953   Max.   :0.9802  
##  Training_Time         Date                       
##  Min.   :0.1938   Min.   :2023-03-17 11:26:21.07  
##  1st Qu.:1.5868   1st Qu.:2023-06-15 11:26:21.07  
##  Median :2.7824   Median :2023-09-06 11:26:21.07  
##  Mean   :2.6726   Mean   :2023-10-10 06:23:11.60  
##  3rd Qu.:3.9692   3rd Qu.:2024-01-25 23:26:21.07  
##  Max.   :4.8085   Max.   :2024-08-02 11:26:21.07
##   Algorithm          Framework         Problem_Type       Dataset_Type      
##  Length:52          Length:52          Length:52          Length:52         
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##     Accuracy        Precision          Recall          F1_Score     
##  Min.   :0.5055   Min.   :0.4031   Min.   :0.3011   Min.   :0.4091  
##  1st Qu.:0.7176   1st Qu.:0.5594   1st Qu.:0.5040   1st Qu.:0.5512  
##  Median :0.7539   Median :0.7116   Median :0.6921   Median :0.7091  
##  Mean   :0.7716   Mean   :0.7106   Mean   :0.6606   Mean   :0.6959  
##  3rd Qu.:0.8686   3rd Qu.:0.8712   3rd Qu.:0.8227   3rd Qu.:0.8163  
##  Max.   :0.9974   Max.   :0.9990   Max.   :0.9916   Max.   :0.9977  
##  Training_Time         Date                       
##  Min.   :0.1032   Min.   :2023-03-08 11:26:21.07  
##  1st Qu.:1.1214   1st Qu.:2023-08-07 17:26:21.07  
##  Median :2.1702   Median :2023-10-11 23:26:21.07  
##  Mean   :2.3372   Mean   :2023-11-14 11:54:02.61  
##  3rd Qu.:3.5715   3rd Qu.:2024-02-21 23:26:21.07  
##  Max.   :4.9786   Max.   :2024-09-07 11:26:21.07

Analisis de normalidad de cada variable:

datos_K <- datos %>%
  filter(Problem_Type == "Regression")
names_numeric_vars <- c("Accuracy", "Precision", "F1_Score", "Training_Time", "Recall")
for (var in names_numeric_vars){
  qqnorm(datos_K[[var]], main = paste("QQ Plot -", var))
  qqline(datos_K[[var]], col = "red")
}

Analisis grafico de cada Algoritmo

En los siguientes graficos de cajas y bigotes, se comparara el desempeño de cada algoritmo de inteligencia artificial en problemas de Regresión, esta comparativa manejara unicamente los valores númericos:

datos_precision_regresion <- datos %>%
  filter(Problem_Type == "Regression") %>%
  select(Algorithm, Accuracy) 

boxplot(Accuracy ~ Algorithm, data = datos_precision_regresion, 
        col = c("red", "blue", "green", "yellow"),
        main = "Comparación de Accuracy por Algoritmo en problemas de regresión",
        xlab = "Algoritmo", ylab = "Accuracy")

Uso de cada algoritmo en cada tipo de problema:

Diferencias de los valores medios de la precisión en los algoritmos Neural Network, Random Forest, SVM y K-means.

Como queremos ver si hay diferencias significativas en las medias de F1 score de los 2 mejores algoritmos en tiempo de entrenamiento y con problemas de Regresión, realizaremos una prueba de varianza para comprobar la igualdad de las varianzas, y realizaremos un intervalo de diferencia de medias para comprobar la diferencia entre ambos. Además, se reforzará el análisis usando los gráficos de cajas y bigotes.

Paso 1: Filtramos la base de datos para cada algoritmo con problemas de regresión, luego se calcula el tiempo promedio de entrenamiento y se imprimen los resultados para ver cuales son los 3 mejores.

#Filtrar datos por algoritmo y tipo de problema
Neural_regresion <- datos %>%  filter(Algorithm == "Neural Network" & Problem_Type == "Regression")
RF_regresion <- datos %>% filter(Algorithm == "Random Forest" & Problem_Type == "Regression")
SVM_regresion <- datos %>% filter(Algorithm == "SVM" & Problem_Type == "Regression")
KMeans_regresion <- datos %>% filter(Algorithm == "K-Means" & Problem_Type == "Regression")

#Calcular la media del tiempo de entrenamiento
media_neural <- mean(Neural_regresion$Training_Time, na.rm = TRUE)
media_RF <- mean(RF_regresion$Training_Time, na.rm = TRUE)
media_SVM <- mean(SVM_regresion$Training_Time, na.rm = TRUE)
media_KMeans <- mean(KMeans_regresion$Training_Time, na.rm = TRUE)

#Imprimir los resultados
print(paste("El promedio del tiempo de entrenamiento Neural Network en problemas de regresión es:", media_neural))
## [1] "El promedio del tiempo de entrenamiento Neural Network en problemas de regresión es: 2.59562288262438"
print(paste("El promedio del tiempo de entrenamiento Random Forest en problemas de regresión es:", media_RF))
## [1] "El promedio del tiempo de entrenamiento Random Forest en problemas de regresión es: 2.6725989949959"
print(paste("El promedio del tiempo de entrenamiento SVM en problemas de regresión es:", media_SVM))
## [1] "El promedio del tiempo de entrenamiento SVM en problemas de regresión es: 2.33723401069937"
print(paste("El promedio del tiempo de entrenamiento K-Means en problemas de regresión es:", media_KMeans))
## [1] "El promedio del tiempo de entrenamiento K-Means en problemas de regresión es: 2.57018451039694"

Notamos que los 3 mejores algoritmos con problemas de regresión en orden del mejor al peor son: SVM y K-Means.
Paso 2: Se va a hacer un intervalo para la analizar la diferencia de las medias. Para ello, usaremos vectores con los datos y comprobaremos si las varianzas son iguales:

K<-datos$F1_Score[datos$Algorithm=="K-Means" & datos$Problem_Type == "Regression"]
SVM<-datos$F1_Score[datos$Algorithm=="SVM" & datos$Problem_Type == "Regression"]
var.test(K, SVM)
## 
##  F test to compare two variances
## 
## data:  K and SVM
## F = 1.2822, num df = 49, denom df = 51, p-value = 0.3811
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.7329541 2.2502104
## sample estimates:
## ratio of variances 
##           1.282212

Una vez comprobada la igualdad de las varianzas, procedemos a realizar el intervalo:

K<-datos$F1_Score[datos$Algorithm=="K-Means" & datos$Problem_Type == "Regression"]
SVM<-datos$F1_Score[datos$Algorithm=="SVM" & datos$Problem_Type == "Regression"]
t.test(K, SVM, var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  K and SVM
## t = 0.44424, df = 100, p-value = 0.6578
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05362213  0.08456391
## sample estimates:
## mean of x mean of y 
## 0.7113910 0.6959201

Los resultados de la prueba indican que el p-valor es mayor a 0.05, lo cual no rechaza la hipotesis nula de que las medias no son diferentes, además, el 0 se encuentra dentro del intervalo, por lo cual se concluye que no hay suficiente evidencia para confirmar que las medias de sus puntajes F1 son diferentes.

Además, analizaremos los gráficos de cajas y bigotes para notar las similutudes de los 3 grupos.

datos_filtrados <- datos[datos$Problem_Type == "Regression" & datos$Algorithm != "Neural Network" & datos$Algorithm != "Random Forest",]
boxplot(F1_Score ~ Algorithm, data = datos_filtrados, 
        col = c("blue", "green"),
        main = "Comparación de F1 score por Algoritmo en problemas de regresión",
        xlab = "Algoritmo", ylab = "F1 score")

Conclusión

Podemos concluir que no existen diferencias estadísticamente significativas entre los valores medios de los grupos involucrados. Sin embargo, se puede notar que SVM es el algoritmo más usado debido a que tiene un menor tiempo de entrenamiento promedio.

Otras Inferencias

Con esta base ya creada, surge la posibilidad de estudiar como es el desempeño de cada modelo en cada tipo de problema, tambien seria interesante investigar otros enfoques dentro del mismo tipo de problema, como por ejemplo indagar sobre que Algoritmo es capaz de presentar la meyor precision en el menor tiempo posible, ya que cada uno de estos modelos responde de manera diferente a cada problema.