Paso 1: Exploración de los datos
- Exploración de la variable Quality
- Densidad
Paso 2: Preparación de los datos para el análisis
Paso 3: Entrenamiento del modelo Regression Tree
- Visualización del árbol de decisión
Paso 4: Evaluación del modelo
- Mean Absolute Error (MAE)
Paso 5: Mejora del modelo
- Mejora en términos de MAE
- Prueba
Referencias

Paso 1: Exploración de los datos

Los datos que analizamos son datos de vinos blancos de Portugal. El conjunto de datos se puede acceder en Repositorio de Datos de Aprendizaje Automático de UCI http://archive.ics.uci.edu/ml

url  <-"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
wine <- read.table(file=url, header = T, sep=";")
knitr::kable(head(wine),caption = "Resumen de datos")

Resumen de datos
fixed.acidity	volatile.acidity	citric.acid	residual.sugar	chlorides	free.sulfur.dioxide	total.sulfur.dioxide	density	pH	sulphates	alcohol	quality
7.0	0.27	0.36	20.7	0.045	45	170	1.0010	3.00	0.45	8.8	6
6.3	0.30	0.34	1.6	0.049	14	132	0.9940	3.30	0.49	9.5	6
8.1	0.28	0.40	6.9	0.050	30	97	0.9951	3.26	0.44	10.1	6
7.2	0.23	0.32	8.5	0.058	47	186	0.9956	3.19	0.40	9.9	6
7.2	0.23	0.32	8.5	0.058	47	186	0.9956	3.19	0.40	9.9	6
8.1	0.28	0.40	6.9	0.050	30	97	0.9951	3.26	0.44	10.1	6

str(wine)

## 'data.frame':    4898 obs. of  12 variables:
##  $ fixed.acidity       : num  7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ...
##  $ volatile.acidity    : num  0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...
##  $ citric.acid         : num  0.36 0.34 0.4 0.32 0.32 0.4 0.16 0.36 0.34 0.43 ...
##  $ residual.sugar      : num  20.7 1.6 6.9 8.5 8.5 6.9 7 20.7 1.6 1.5 ...
##  $ chlorides           : num  0.045 0.049 0.05 0.058 0.058 0.05 0.045 0.045 0.049 0.044 ...
##  $ free.sulfur.dioxide : num  45 14 30 47 47 30 30 45 14 28 ...
##  $ total.sulfur.dioxide: num  170 132 97 186 186 97 136 170 132 129 ...
##  $ density             : num  1.001 0.994 0.995 0.996 0.996 ...
##  $ pH                  : num  3 3.3 3.26 3.19 3.19 3.26 3.18 3 3.3 3.22 ...
##  $ sulphates           : num  0.45 0.49 0.44 0.4 0.4 0.44 0.47 0.45 0.49 0.45 ...
##  $ alcohol             : num  8.8 9.5 10.1 9.9 9.9 10.1 9.6 8.8 9.5 11 ...
##  $ quality             : int  6 6 6 6 6 6 6 6 6 6 ...

library(plotly)
plot_ly(data = wine, x =~quality, type = "histogram")

Queremos predecir la calidad del vino, Quality según otras características del mismo:

acidity
sugar content
chlorides
sulfur
alcohol
pH
density

Exploración de la variable `Quality`

wine2 <- wine
wine2$qualitychar <- ifelse( wine2$quality == 3, "a_tres"
                             , ifelse(wine2$quality == 4, "b_Cuatro"
                             , ifelse(wine2$quality == 5, "c_cinco"
                             , ifelse(wine2$quality == 6, "d_Seis"
                             , ifelse(wine2$quality == 7, "e_Siete"
                             , ifelse(wine2$quality == 8, "f_Ocho"
                             , "g_Nueve"))) )))

plot_ly(data = wine2, x = ~qualitychar, y = ~alcohol
        , color = ~qualitychar
        , type = "box"
        , colors = "Dark2"
        )

Quality sigue una distribución más o menos Normal.

Densidad

plot_ly(data = wine2, x = ~qualitychar, y = ~density
        , color = ~qualitychar, type = "box", colors = "Set1")

Paso 2: Preparación de los datos para el análisis

No es necesario el procesamiento previo de datos para emplear un modelo de árbol de decisión. Para los datos de entrenamiento (training) y prueba (test) tomar una muestra al azar con una proporción del 75% y del 25% respectivamente.

# training set
set.seed(pi)
itrain     <- sample( 1:4898, size=3750, replace = FALSE)
wine_train <- wine[itrain, ]
nrow(wine_train)

## [1] 3750

# test set
wine_test  <- wine[-itrain, ]

Paso 3: Entrenamiento del modelo Regression Tree

El paquete rpart (partición recursiva) ofrece una muy buena implementación árboles de regresión CART.

library(rpart)

m.rpart <- rpart(quality ~. 
                 , data = wine_train)
m.rpart

## n= 3750 
## 
## node), split, n, deviance, yval
##       * denotes terminal node
## 
##  1) root 3750 2933.71800 5.880800  
##    2) alcohol< 10.85 2374 1431.89400 5.607835  
##      4) volatile.acidity>=0.235 1488  738.64250 5.412634  
##        8) free.sulfur.dioxide< 13.5 132   69.51515 4.939394 *
##        9) free.sulfur.dioxide>=13.5 1356  636.68730 5.458702  
##         18) alcohol< 9.85 882  335.83670 5.346939 *
##         19) alcohol>=9.85 474  269.33330 5.666667 *
##      5) volatile.acidity< 0.235 886  541.33300 5.935666 *
##    3) alcohol>=10.85 1376 1019.75600 6.351744  
##      6) alcohol< 11.74167 664  501.30120 6.114458  
##       12) free.sulfur.dioxide< 10.5 36   38.00000 5.000000 *
##       13) free.sulfur.dioxide>=10.5 628  416.02550 6.178344 *
##      7) alcohol>=11.74167 712  446.20220 6.573034 *

Visualización del árbol de decisión

library(rpart.plot)     
rpart.plot(m.rpart)

Paso 4: Evaluación del modelo

p.rpart <- predict( m.rpart, wine_test )

summary(p.rpart)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.939   5.347   5.936   5.888   6.178   6.573

summary( wine_test$quality )

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.000   5.000   6.000   5.868   6.000   9.000

Mean Absolute Error (MAE)

Otra forma de analizar el rendimiento del modelo es considerar cómo de diferente, en promedio, ha caído la predicción del valor real: Error absoluto medio (MAE).

MAE <- function(actual, predicted){
  mean(abs (actual - predicted))
}

MAE(wine_test$quality, p.rpart)

## [1] 0.5971976

Un Error Absoluto Medio del 59% es aceptable.

Paso 5: Mejora del modelo

Para mejorar el modelo pensamos en implementar otro algoritmo, un model tree, este ejecuta múltiples modelos de regresión en cada nodo.

M5P (where the P stands for ‘prime’) generates M5 model trees using the M5’ algorithm, which was introduced in Wang & Witten (1997) and enhances the original M5 algorithm by Quinlan (1992).

# ajuste
m.m5p <- M5P(quality ~. , data = wine_train)

# Construimos un predictor
p.m5p <- predict(m.m5p, wine_test)

m.m5p

## M5 pruned model tree:
## (using smoothed linear models)
## 
## alcohol <= 10.85 : 
## |   volatile.acidity <= 0.275 : 
## |   |   volatile.acidity <= 0.207 : 
## |   |   |   residual.sugar <= 12.575 : LM1 (452/80.189%)
## |   |   |   residual.sugar >  12.575 : 
## |   |   |   |   alcohol <= 9.05 : 
## |   |   |   |   |   free.sulfur.dioxide <= 30.5 : LM2 (18/0%)
## |   |   |   |   |   free.sulfur.dioxide >  30.5 : 
## |   |   |   |   |   |   fixed.acidity <= 7.15 : LM3 (8/37.391%)
## |   |   |   |   |   |   fixed.acidity >  7.15 : 
## |   |   |   |   |   |   |   citric.acid <= 0.275 : LM4 (6/0%)
## |   |   |   |   |   |   |   citric.acid >  0.275 : 
## |   |   |   |   |   |   |   |   pH <= 2.995 : LM5 (8/0%)
## |   |   |   |   |   |   |   |   pH >  2.995 : 
## |   |   |   |   |   |   |   |   |   citric.acid <= 0.305 : LM6 (5/0%)
## |   |   |   |   |   |   |   |   |   citric.acid >  0.305 : 
## |   |   |   |   |   |   |   |   |   |   total.sulfur.dioxide <= 152.5 : LM7 (3/0%)
## |   |   |   |   |   |   |   |   |   |   total.sulfur.dioxide >  152.5 : LM8 (4/48.956%)
## |   |   |   |   alcohol >  9.05 : 
## |   |   |   |   |   citric.acid <= 0.295 : LM9 (15/0%)
## |   |   |   |   |   citric.acid >  0.295 : 
## |   |   |   |   |   |   chlorides <= 0.052 : 
## |   |   |   |   |   |   |   density <= 0.997 : 
## |   |   |   |   |   |   |   |   density <= 0.997 : LM10 (3/53.297%)
## |   |   |   |   |   |   |   |   density >  0.997 : LM11 (6/0%)
## |   |   |   |   |   |   |   density >  0.997 : 
## |   |   |   |   |   |   |   |   density <= 0.999 : LM12 (20/0%)
## |   |   |   |   |   |   |   |   density >  0.999 : 
## |   |   |   |   |   |   |   |   |   citric.acid <= 0.355 : LM13 (3/0%)
## |   |   |   |   |   |   |   |   |   citric.acid >  0.355 : LM14 (11/72.677%)
## |   |   |   |   |   |   chlorides >  0.052 : 
## |   |   |   |   |   |   |   citric.acid <= 0.33 : LM15 (7/0%)
## |   |   |   |   |   |   |   citric.acid >  0.33 : 
## |   |   |   |   |   |   |   |   fixed.acidity <= 7.35 : LM16 (2/0%)
## |   |   |   |   |   |   |   |   fixed.acidity >  7.35 : LM17 (2/0%)
## |   |   volatile.acidity >  0.207 : LM18 (794/80.012%)
## |   volatile.acidity >  0.275 : LM19 (1007/70.185%)
## alcohol >  10.85 : 
## |   alcohol <= 11.742 : 
## |   |   free.sulfur.dioxide <= 19.5 : LM20 (139/99.551%)
## |   |   free.sulfur.dioxide >  19.5 : LM21 (525/85.946%)
## |   alcohol >  11.742 : LM22 (712/82.921%)
## 
## LM num: 1
## quality = 
##  0.224 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.0252 * citric.acid 
##  + 0.1675 * residual.sugar 
##  + 3.6926 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 344.0395 * density 
##  + 1.4353 * pH 
##  + 1.1799 * sulphates 
##  - 0.1373 * alcohol 
##  + 341.6078
## 
## LM num: 2
## quality = 
##  0.2851 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 1.3837 * citric.acid 
##  + 0.0009 * residual.sugar 
##  + 6.5209 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.1324 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 39.1688
## 
## LM num: 3
## quality = 
##  0.4128 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 2.1858 * citric.acid 
##  + 0.0009 * residual.sugar 
##  + 6.5209 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.1324 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 38.353
## 
## LM num: 4
## quality = 
##  0.0945 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 3.2342 * citric.acid 
##  + 0.0009 * residual.sugar 
##  + 6.5209 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  - 0.0028 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  + 1.1023 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 38.1374
## 
## LM num: 5
## quality = 
##  0.0786 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 3.1743 * citric.acid 
##  + 0.0009 * residual.sugar 
##  + 6.5209 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  - 0.0017 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  + 1.1553 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 37.7903
## 
## LM num: 6
## quality = 
##  0.095 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 3.6989 * citric.acid 
##  + 0.0009 * residual.sugar 
##  + 6.5209 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  - 0.0031 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  + 1.0743 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 38.4185
## 
## LM num: 7
## quality = 
##  0.095 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 3.6436 * citric.acid 
##  + 0.0009 * residual.sugar 
##  + 6.5209 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  - 0.0037 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  + 1.0743 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 38.4799
## 
## LM num: 8
## quality = 
##  0.095 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 3.6436 * citric.acid 
##  + 0.0009 * residual.sugar 
##  + 6.5209 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  - 0.0037 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  + 1.0743 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 38.4633
## 
## LM num: 9
## quality = 
##  0.0914 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.261 * citric.acid 
##  - 0.0158 * residual.sugar 
##  + 5.2945 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 39.7253
## 
## LM num: 10
## quality = 
##  0.0914 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.4809 * citric.acid 
##  - 0.0043 * residual.sugar 
##  + 8.5031 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  + 59.3298 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  - 53.175
## 
## LM num: 11
## quality = 
##  0.0914 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.4809 * citric.acid 
##  - 0.0043 * residual.sugar 
##  + 8.5031 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  + 46.0329 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  - 39.8825
## 
## LM num: 12
## quality = 
##  0.0914 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.6526 * citric.acid 
##  + 0.0069 * residual.sugar 
##  + 8.5031 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 39.405
## 
## LM num: 13
## quality = 
##  0.0914 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.6881 * citric.acid 
##  + 0.0314 * residual.sugar 
##  + 8.5031 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 39.0305
## 
## LM num: 14
## quality = 
##  0.0914 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.6881 * citric.acid 
##  + 0.0246 * residual.sugar 
##  + 8.5031 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 39.1027
## 
## LM num: 15
## quality = 
##  -0.0366 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.7517 * citric.acid 
##  - 0.0043 * residual.sugar 
##  + 12.4522 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 40.6239
## 
## LM num: 16
## quality = 
##  -0.0568 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.7517 * citric.acid 
##  - 0.0043 * residual.sugar 
##  + 12.4522 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 40.749
## 
## LM num: 17
## quality = 
##  -0.0568 * fixed.acidity 
##  - 0.1521 * volatile.acidity 
##  - 0.7517 * citric.acid 
##  - 0.0043 * residual.sugar 
##  + 12.4522 * chlorides 
##  + 0 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 33.7487 * density 
##  - 0.0831 * pH 
##  + 0.1277 * sulphates 
##  - 0.0234 * alcohol 
##  + 40.7432
## 
## LM num: 18
## quality = 
##  0.0768 * fixed.acidity 
##  - 2.9002 * volatile.acidity 
##  - 0.0059 * citric.acid 
##  + 0.0546 * residual.sugar 
##  + 0.0335 * chlorides 
##  + 0.0023 * free.sulfur.dioxide 
##  + 0 * total.sulfur.dioxide 
##  - 130.8842 * density 
##  + 0.3813 * pH 
##  + 0.7226 * sulphates 
##  + 0.1358 * alcohol 
##  + 132.7272
## 
## LM num: 19
## quality = 
##  0.0016 * fixed.acidity 
##  - 1.1871 * volatile.acidity 
##  + 0.0548 * residual.sugar 
##  + 0.0025 * free.sulfur.dioxide 
##  - 99.3856 * density 
##  + 0.6052 * pH 
##  + 0.4919 * sulphates 
##  + 0.1314 * alcohol 
##  + 100.7473
## 
## LM num: 20
## quality = 
##  0.0039 * fixed.acidity 
##  - 3.445 * volatile.acidity 
##  + 0.0698 * citric.acid 
##  + 0.1353 * residual.sugar 
##  - 0.587 * chlorides 
##  + 0.0705 * free.sulfur.dioxide 
##  - 321.2688 * density 
##  + 0.1402 * pH 
##  + 0.0758 * sulphates 
##  + 0.0036 * alcohol 
##  + 323.5085
## 
## LM num: 21
## quality = 
##  0.0039 * fixed.acidity 
##  - 1.3212 * volatile.acidity 
##  + 0.0199 * citric.acid 
##  + 0.0802 * residual.sugar 
##  - 5.9846 * chlorides 
##  + 0.0003 * free.sulfur.dioxide 
##  - 0.0016 * total.sulfur.dioxide 
##  - 96.9542 * density 
##  + 0.9579 * pH 
##  + 0.6961 * sulphates 
##  + 0.0036 * alcohol 
##  + 99.3517
## 
## LM num: 22
## quality = 
##  0.1596 * fixed.acidity 
##  - 0.0393 * volatile.acidity 
##  + 0.1054 * residual.sugar 
##  - 6.2377 * chlorides 
##  + 0.0148 * free.sulfur.dioxide 
##  - 258.2448 * density 
##  + 1.3379 * pH 
##  + 0.6299 * sulphates 
##  + 0.0034 * alcohol 
##  + 256.083
## 
## Number of Rules : 22

Mejora en términos de MAE

MAE( wine_test$quality, p.m5p )

## [1] 0.5765669

de 59% a 57% hemos mejorado la estimación.

Prueba

Podemos estimar la calidad de un vino concreto que tenga los siguientes valores en las variables:

test <- data.frame(fixed.acidity = 8.5, volatile.acidity = 0.33
                   , citric.acid = 0.42, residual.sugar = 10.5
                   , chlorides = 0.065, free.sulfur.dioxide = 47
                   , total.sulfur.dioxide = 186, density = 0.9955
                   , pH = 3.10, sulphates = 0.40, alcohol = 9.9)

test_pred <- predict(m.m5p, test)

test_pred

## [1] 5.496149

Referencias

Hornik K, Buchta C and Zeileis A (2009). “Open-Source Machine Learning: R Meets Weka.” Computational Statistics, 24(2), pp. 225-232. doi:10.1007/s00180-008-0119-7 (URL:http://doi.org/10.1007/s00180-008-0119-7).
Terry Therneau and Beth Atkinson (2018). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-12. https://CRAN.R-project.org/package=rpart
Carson Sievert, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec and Pedro Despouy (2017). plotly: Create Interactive Web Graphics via ‘plotly.js’. R package version 4.7.1. https://CRAN.R-project.org/package=plotly
Jason Chan. Estimating Wine Quality with Decision Tree (2016). http://www.rpubs.com/jasonchanhku/wine

sessionInfo()

## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
## 
## locale:
##  [1] LC_CTYPE=es_ES.UTF-8          LC_NUMERIC=C                 
##  [3] LC_TIME=es_ES.UTF-8           LC_COLLATE=es_ES.UTF-8       
##  [5] LC_MONETARY=es_ES.UTF-8       LC_MESSAGES=es_ES.UTF-8      
##  [7] LC_PAPER=es_ES.UTF-8          LC_NAME=es_ES.UTF-8          
##  [9] LC_ADDRESS=es_ES.UTF-8        LC_TELEPHONE=es_ES.UTF-8     
## [11] LC_MEASUREMENT=es_ES.UTF-8    LC_IDENTIFICATION=es_ES.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] bindrcpp_0.2       RWeka_0.4-37       RColorBrewer_1.1-2
## [4] rpart.plot_2.1.2   plotly_4.7.1       ggplot2_2.2.1     
## [7] rpart_4.1-12      
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.12      compiler_3.4.3    plyr_1.8.4       
##  [4] bindr_0.1         RWekajars_3.9.2-1 tools_3.4.3      
##  [7] digest_0.6.12     jsonlite_1.5      evaluate_0.10.1  
## [10] tibble_1.3.4      gtable_0.2.0      viridisLite_0.2.0
## [13] pkgconfig_2.0.1   rlang_0.1.2       shiny_1.0.5      
## [16] crosstalk_1.0.0   yaml_2.1.14       rJava_0.9-8      
## [19] stringr_1.2.0     dplyr_0.7.4       httr_1.3.1       
## [22] knitr_1.17        htmlwidgets_0.9   rprojroot_1.2    
## [25] grid_3.4.3        glue_1.1.1        data.table_1.10.4
## [28] R6_2.2.2          rmarkdown_1.6     purrr_0.2.4      
## [31] tidyr_0.8.0       magrittr_1.5      backports_1.1.0  
## [34] scales_0.5.0      htmltools_0.3.6   assertthat_0.2.0 
## [37] xtable_1.8-2      mime_0.5          colorspace_1.3-2 
## [40] httpuv_1.3.5      stringi_1.1.5     lazyeval_0.2.0   
## [43] munsell_0.4.3

Ejemplo de clasificación por árboles de decisión

Estimación de la calidad del Vino (Wine Quality)

6 de febrero de 2018

Paso 1: Exploración de los datos

Exploración de la variable `Quality`

Densidad

Paso 2: Preparación de los datos para el análisis

Paso 3: Entrenamiento del modelo Regression Tree

Visualización del árbol de decisión

Paso 4: Evaluación del modelo

Mean Absolute Error (MAE)

Paso 5: Mejora del modelo

Mejora en términos de MAE

Prueba

Referencias

Ejemplo de clasificación por árboles de decisión

Estimación de la calidad del Vino (Wine Quality)

6 de febrero de 2018

Paso 1: Exploración de los datos

Exploración de la variable Quality

Densidad

Paso 2: Preparación de los datos para el análisis

Paso 3: Entrenamiento del modelo Regression Tree

Visualización del árbol de decisión

Paso 4: Evaluación del modelo

Mean Absolute Error (MAE)

Paso 5: Mejora del modelo

Mejora en términos de MAE

Prueba

Referencias

Exploración de la variable `Quality`