Caso 13. Maquinas de Soporte Vectorial (SVM). Datos de Ingresos personas USA

Objetivo

Aplicar e interpretar el algoritmo de maquinas de soporte vectorial SVM con los datos de personas e ingresos de USA y comparar con el caso 11 de regresión loígistica el valor de exactitud.

Descripción

Construir un modelo de de maquinas de soporte vectorial SVM aplicado a datos de personas y sus ingresos en USA

La variable dependiente es los ingresos identificado por 0 y 1, los ganan por debajo o igual a 50 Mil y los que ganan por encima de 50 Mil.

Fundamento teórico

Pendiente que es el algoritmo de MSV ?

Proceso

• Cargar librerías
• Cargar datos
• Identificar variables
• Crear datos de entrenamiento y validación
• Crear modelo de Máquinas de Soporte Vectorial SVM
• Deteminar anticipadamente e mejor costo del modelo de SVM Pendiente
• Analizar y/o describir el modelo
• Realizar predicciones con el conjunto de datos de validación
• Evaluar el modelo de predicción con matriz de confusión
• Interpretar el caso
• Comparar con el algoritmo de regresión logística del caso 11.

1. Cargar librerías

• Posiblemente se utilicen algunas de ellas

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(knitr)
library(caret)

## Warning: package 'caret' was built under R version 4.0.3

## Loading required package: lattice

library(readr)
library(knitr)
library(e1071)

2. Cargar datos

datos <- read.csv("https://raw.githubusercontent.com/rpizarrog/FundamentosMachineLearning/master/datos/adultos_clean.csv", encoding = "UTF-8")
kable(head(datos, 10), caption = "Los primeros 10 registros de datos")

Los primeros 10 registros de datos

X	age	workclass	education	educational.num	marital.status	race	gender	hours.per.week	income	age.scale	educational.num.scale	hours.per.week.scale	income10
1	25	Private	Dropout	7	Not_married	Black	Male	40	<=50K	0.1095890	0.4000000	0.3979592	0
2	38	Private	HighGrad	9	Married	White	Male	50	<=50K	0.2876712	0.5333333	0.5000000	0
3	28	Local-gov	Community	12	Married	White	Male	40	>50K	0.1506849	0.7333333	0.3979592	1
4	44	Private	Community	10	Married	Black	Male	40	>50K	0.3698630	0.6000000	0.3979592	1
5	18	?	Community	10	Not_married	White	Female	30	<=50K	0.0136986	0.6000000	0.2959184	0
6	34	Private	Dropout	6	Not_married	White	Male	30	<=50K	0.2328767	0.3333333	0.2959184	0
7	29	?	HighGrad	9	Not_married	Black	Male	40	<=50K	0.1643836	0.5333333	0.3979592	0
8	63	Self-emp-not-inc	Master	15	Married	White	Male	32	>50K	0.6301370	0.9333333	0.3163265	1
9	24	Private	Community	10	Not_married	White	Female	40	<=50K	0.0958904	0.6000000	0.3979592	0
10	55	Private	Dropout	4	Married	White	Male	10	<=50K	0.5205479	0.2000000	0.0918367	0

3. Las variables Convertir a factor la variable dependiente income10

datos$income10 <- factor(datos$income10)

str(datos)

## 'data.frame':    48842 obs. of  14 variables:
##  $ X                    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ age                  : int  25 38 28 44 18 34 29 63 24 55 ...
##  $ workclass            : chr  "Private" "Private" "Local-gov" "Private" ...
##  $ education            : chr  "Dropout" "HighGrad" "Community" "Community" ...
##  $ educational.num      : int  7 9 12 10 10 6 9 15 10 4 ...
##  $ marital.status       : chr  "Not_married" "Married" "Married" "Married" ...
##  $ race                 : chr  "Black" "White" "White" "Black" ...
##  $ gender               : chr  "Male" "Male" "Male" "Male" ...
##  $ hours.per.week       : int  40 50 40 40 30 30 40 32 40 10 ...
##  $ income               : chr  "<=50K" "<=50K" ">50K" ">50K" ...
##  $ age.scale            : num  0.1096 0.2877 0.1507 0.3699 0.0137 ...
##  $ educational.num.scale: num  0.4 0.533 0.733 0.6 0.6 ...
##  $ hours.per.week.scale : num  0.398 0.5 0.398 0.398 0.296 ...
##  $ income10             : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 2 1 1 ...

• age la edad de la persona
• workclass es un tipo o clase de trabajo de la persona, privado, gobierno, por su cuenta,
• education indica el nivel educativo de la persona
• educational es el valor numérico de education
• marital.status es su estado civil
• race es el tipo de raza de persona
• gender es el género de la persona
• hours.per.week son las horas que trbaja por semana
• income son los ingresos
• age_scale la edad escalada
• hours.per.week escalada
• income10 con valores de 0 gana menos de 50 mil y 1 mas de 50 mil

4. Crear datos de entrenamiento y validación

• 70 % datos de entrenamiento
• 30 % datos de validación

set.seed(2020)
entrena <- createDataPartition(y = datos$income10, p = 0.7, list = FALSE, times = 1)

# Datos entrenamiento
datos.entrenamiento <- datos[entrena, ]  # [renglones, columna]

# Datos validación
datos.validacion <- datos[-entrena, ]

kable(head(datos.entrenamiento, 10), caption = "Datos de entrenamiento  (primeros diez)", row.names = 1:nrow(datos.entrenamiento))

## Warning in if (is.na(row.names)) row.names = has_rownames(x): la condición tiene
## longitud > 1 y sólo el primer elemento será usado

## Warning in if (row.names) {: la condición tiene longitud > 1 y sólo el primer
## elemento será usado

Datos de entrenamiento (primeros diez)

	X	age	workclass	education	educational.num	marital.status	race	gender	hours.per.week	income	age.scale	educational.num.scale	hours.per.week.scale	income10
1	1	25	Private	Dropout	7	Not_married	Black	Male	40	<=50K	0.1095890	0.4000000	0.3979592	0
2	2	38	Private	HighGrad	9	Married	White	Male	50	<=50K	0.2876712	0.5333333	0.5000000	0
3	3	28	Local-gov	Community	12	Married	White	Male	40	>50K	0.1506849	0.7333333	0.3979592	1
4	4	44	Private	Community	10	Married	Black	Male	40	>50K	0.3698630	0.6000000	0.3979592	1
5	5	18	?	Community	10	Not_married	White	Female	30	<=50K	0.0136986	0.6000000	0.2959184	0
7	7	29	?	HighGrad	9	Not_married	Black	Male	40	<=50K	0.1643836	0.5333333	0.3979592	0
8	8	63	Self-emp-not-inc	Master	15	Married	White	Male	32	>50K	0.6301370	0.9333333	0.3163265	1
9	9	24	Private	Community	10	Not_married	White	Female	40	<=50K	0.0958904	0.6000000	0.3979592	0
10	10	55	Private	Dropout	4	Married	White	Male	10	<=50K	0.5205479	0.2000000	0.0918367	0
11	11	65	Private	HighGrad	9	Married	White	Male	40	>50K	0.6575342	0.5333333	0.3979592	1

kable(head(datos.validacion, 10), caption = "Datos de validación  (primeros diez)", row.names = 1:nrow(datos.entrenamiento))

## Warning in if (is.na(row.names)) row.names = has_rownames(x): la condición tiene
## longitud > 1 y sólo el primer elemento será usado

## Warning in if (row.names) {: la condición tiene longitud > 1 y sólo el primer
## elemento será usado

Datos de validación (primeros diez)

	X	age	workclass	education	educational.num	marital.status	race	gender	hours.per.week	income	age.scale	educational.num.scale	hours.per.week.scale	income10
6	6	34	Private	Dropout	6	Not_married	White	Male	30	<=50K	0.2328767	0.3333333	0.2959184	0
15	15	48	Private	HighGrad	9	Married	White	Male	48	>50K	0.4246575	0.5333333	0.4795918	1
17	17	20	State-gov	Community	10	Not_married	White	Male	25	<=50K	0.0410959	0.6000000	0.2448980	0
36	36	65	?	HighGrad	9	Married	White	Male	40	<=50K	0.6575342	0.5333333	0.3979592	0
41	41	65	Private	Master	14	Married	White	Male	50	>50K	0.6575342	0.8666667	0.5000000	1
49	49	52	Private	Dropout	7	Separated	Black	Female	18	<=50K	0.4794521	0.4000000	0.1734694	0
51	51	18	Private	Community	10	Not_married	White	Male	20	<=50K	0.0136986	0.6000000	0.1938776	0
54	54	22	Private	HighGrad	9	Not_married	White	Male	60	>50K	0.0684932	0.5333333	0.6020408	1
56	56	21	Private	Community	10	Not_married	White	Female	40	<=50K	0.0547945	0.6000000	0.3979592	0
58	58	34	Local-gov	Bachelors	13	Married	White	Male	50	>50K	0.2328767	0.8000000	0.5000000	1

5. Crear modelo de Máquinas de Soporte Vectorial SVM

• Con el paquete e1071 se genera el modelo de SVM
• Se utiliza la función svm
• Kernel lineal

modelo1 <- svm(income10 ~ ., data = datos.entrenamiento, kernel = "linear", scale = TRUE, cost = .05)

6. Analizar y/o describir el modelo

summary(modelo1)

## 
## Call:
## svm(formula = income10 ~ ., data = datos.entrenamiento, kernel = "linear", 
##     cost = 0.05, scale = TRUE)
## 
## 
## Parameters:
##    SVM-Type:  C-classification 
##  SVM-Kernel:  linear 
##        cost:  0.05 
## 
## Number of Support Vectors:  246
## 
##  ( 143 103 )
## 
## 
## Number of Classes:  2 
## 
## Levels: 
##  0 1

7. Realizar predicciones con el conjunto de datos de validación

prediccion <- predict(modelo1, datos.validacion)

• Agregando columna al final de datos de validación para comparar

datos.validacion <- cbind(datos.validacion, prediccion = prediccion)

kable(head(datos.validacion, 50), caption = "Las predicciones, primeros 10 registros")

Las predicciones, primeros 10 registros

	X	age	workclass	education	educational.num	marital.status	race	gender	hours.per.week	income	age.scale	educational.num.scale	hours.per.week.scale	income10	prediccion
6	6	34	Private	Dropout	6	Not_married	White	Male	30	<=50K	0.2328767	0.3333333	0.2959184	0	0
15	15	48	Private	HighGrad	9	Married	White	Male	48	>50K	0.4246575	0.5333333	0.4795918	1	1
17	17	20	State-gov	Community	10	Not_married	White	Male	25	<=50K	0.0410959	0.6000000	0.2448980	0	0
36	36	65	?	HighGrad	9	Married	White	Male	40	<=50K	0.6575342	0.5333333	0.3979592	0	0
41	41	65	Private	Master	14	Married	White	Male	50	>50K	0.6575342	0.8666667	0.5000000	1	1
49	49	52	Private	Dropout	7	Separated	Black	Female	18	<=50K	0.4794521	0.4000000	0.1734694	0	0
51	51	18	Private	Community	10	Not_married	White	Male	20	<=50K	0.0136986	0.6000000	0.1938776	0	0
54	54	22	Private	HighGrad	9	Not_married	White	Male	60	>50K	0.0684932	0.5333333	0.6020408	1	1
56	56	21	Private	Community	10	Not_married	White	Female	40	<=50K	0.0547945	0.6000000	0.3979592	0	0
58	58	34	Local-gov	Bachelors	13	Married	White	Male	50	>50K	0.2328767	0.8000000	0.5000000	1	1
59	59	42	Self-emp-inc	HighGrad	9	Married	White	Male	50	>50K	0.3424658	0.5333333	0.5000000	1	1
61	61	30	Private	Bachelors	13	Not_married	White	Female	50	<=50K	0.1780822	0.8000000	0.5000000	0	0
64	64	33	Private	HighGrad	9	Not_married	White	Female	40	<=50K	0.2191781	0.5333333	0.3979592	0	0
68	68	19	Private	Community	10	Not_married	White	Male	20	<=50K	0.0273973	0.6000000	0.1938776	0	0
74	74	21	Private	Community	10	Separated	White	Female	40	<=50K	0.0547945	0.6000000	0.3979592	0	0
77	77	41	Private	HighGrad	9	Married	White	Male	50	<=50K	0.3287671	0.5333333	0.5000000	0	0
79	79	50	Private	HighGrad	9	Married	White	Male	40	<=50K	0.4520548	0.5333333	0.3979592	0	0
81	81	45	Self-emp-inc	Community	10	Married	White	Male	50	<=50K	0.3835616	0.6000000	0.5000000	0	0
91	91	59	Private	Bachelors	13	Not_married	White	Female	25	<=50K	0.5753425	0.8000000	0.2448980	0	0
95	95	34	Private	Master	14	Not_married	Amer-Indian-Eskimo	Male	40	<=50K	0.2328767	0.8666667	0.3979592	0	0
96	96	20	Private	HighGrad	9	Not_married	White	Male	40	<=50K	0.0410959	0.5333333	0.3979592	0	0
97	97	25	Private	Bachelors	13	Not_married	White	Female	40	<=50K	0.1095890	0.8000000	0.3979592	0	0
102	102	33	Private	Community	10	Not_married	Black	Female	35	<=50K	0.2191781	0.6000000	0.3469388	0	0
111	111	18	Private	HighGrad	9	Not_married	White	Female	48	<=50K	0.0136986	0.5333333	0.4795918	0	0
112	112	28	Private	Community	10	Married	White	Male	40	<=50K	0.1506849	0.6000000	0.3979592	0	0
120	120	43	Private	Bachelors	13	Separated	White	Female	40	>50K	0.3561644	0.8000000	0.3979592	1	1
123	123	19	Private	Community	10	Not_married	White	Male	30	<=50K	0.0273973	0.6000000	0.2959184	0	0
129	129	27	Self-emp-not-inc	HighGrad	9	Married	White	Male	60	>50K	0.1369863	0.5333333	0.6020408	1	1
131	131	41	Private	Community	10	Married	White	Male	40	<=50K	0.3287671	0.6000000	0.3979592	0	0
135	135	57	Private	HighGrad	9	Married	Black	Male	48	<=50K	0.5479452	0.5333333	0.4795918	0	0
141	141	46	Private	Master	14	Married	White	Male	40	<=50K	0.3972603	0.8666667	0.3979592	0	0
144	144	43	Self-emp-inc	HighGrad	9	Married	White	Male	45	>50K	0.3561644	0.5333333	0.4489796	1	1
145	145	34	Private	Master	14	Not_married	White	Female	30	<=50K	0.2328767	0.8666667	0.2959184	0	0
147	147	44	Private	Community	11	Widow	White	Female	30	<=50K	0.3698630	0.6666667	0.2959184	0	0
153	153	50	Private	Dropout	4	Married	White	Male	20	<=50K	0.4520548	0.2000000	0.1938776	0	0
160	160	38	Self-emp-inc	Bachelors	13	Separated	White	Male	40	<=50K	0.2876712	0.8000000	0.3979592	0	0
161	161	55	Private	Dropout	7	Married	White	Male	30	<=50K	0.5205479	0.4000000	0.2959184	0	0
163	163	22	Private	HighGrad	9	Married	White	Male	45	<=50K	0.0684932	0.5333333	0.4489796	0	0
165	165	46	State-gov	Master	14	Married	White	Male	45	>50K	0.3972603	0.8666667	0.4489796	1	1
167	167	58	Self-emp-not-inc	PhD	16	Married	White	Male	16	>50K	0.5616438	1.0000000	0.1530612	1	1
168	168	42	Private	HighGrad	9	Married	White	Male	45	<=50K	0.3424658	0.5333333	0.4489796	0	0
171	171	54	Private	HighGrad	9	Married	White	Male	40	>50K	0.5068493	0.5333333	0.3979592	1	1
172	172	34	Private	Master	14	Not_married	White	Male	40	<=50K	0.2328767	0.8666667	0.3979592	0	0
173	173	26	Private	Bachelors	13	Not_married	White	Female	40	<=50K	0.1232877	0.8000000	0.3979592	0	0
175	175	48	Local-gov	Master	14	Separated	Black	Female	40	<=50K	0.4246575	0.8666667	0.3979592	0	0
176	176	36	Private	Community	10	Married	White	Male	45	>50K	0.2602740	0.6000000	0.4489796	1	1
185	185	44	Private	HighGrad	9	Separated	Black	Female	40	<=50K	0.3698630	0.5333333	0.3979592	0	0
189	189	34	State-gov	Bachelors	13	Not_married	Black	Male	40	<=50K	0.2328767	0.8000000	0.3979592	0	0
193	193	47	Private	Community	10	Separated	Black	Female	37	<=50K	0.4109589	0.6000000	0.3673469	0	0
196	196	31	Private	Community	10	Separated	White	Female	40	<=50K	0.1917808	0.6000000	0.3979592	0	0

8. Evaluar el modelo de predicción con matriz de confusión

• Generando la matriz de confusión

mat.confusion <- table(predicho  = prediccion, real = datos.validacion$income10)

mat.confusion

##         real
## predicho     0     1
##        0 11146     0
##        1     0  3506