新数据中的预测变量类型与培训数据的类型不符

Question

我想使用随机森林对R中的自杀率（log_suicides_per_100k）进行预测，我遇到的问题是，当我尝试选择一个变量级别时，我会得到错误：

Type of predictors in new data do not match that of the training data. 

模型是：性别是：

rf3 <- randomForest(log_suicides_per_100k~ age+sex+log_gdp_per_capita+log_population+year, # formula data = train, # data ntree = 500)

性别有四个级别：男性和男性和女性 年龄有六个级别； “ 15-24岁”，“ 25 - 34年”，“ 35-54岁”， “ 5-14岁”，“ 55-74岁”，“ 75岁以上”，

structure(list(year = c(2001L, 2004L, 2008L, 2010L, 2004L, 2011L
), sex = structure(c(2L, 2L, 1L, 2L, 2L, 1L), .Label = c("female", 
"male"), class = "factor"), age = structure(c(1L, 6L, 3L, 6L, 
2L, 3L), .Label = c("15-24 years", "25-34 years", "35-54 years", 
"5-14 years", "55-74 years", "75+ years"), class = "factor"), 
log_population = c(14.0462476055718, 10.0651811415341, 
13.5550389013841, 
10.2665669441479, 15.5047227728237, 13.4021140795298), 
log_suicides_per_100k = c(2.42657107277504, 
4.03069453514564, 2.38508631450579, 4.15261347034608, 
2.88480071284671, 
0.647103242058539), log_gdp_per_capita = c(7.67786350067821, 
9.13701670755734, 11.1338150021447, 9.65117262392164, 
7.95472333449791, 
8.14177220465645)), row.names = c(7888L, 8465L, 7593L, 8535L, 
25159L, 9656L), class = "data.frame")

我想预测2025年75岁以上男性的自杀率。

prediction <- predict(rf3, data.frame (age = '75+ years', sex= 'male', log_gdp_per_capita = 13.082, log_population = 9.393, year = 2025))

Answer 1

这是一些有效的代码。因为您还没有包含所有代码，所以有可能对您不起作用的风险。因素和级别需要匹配，因此这是正确的关键。复制培训数据中的因素和水平，并将其设置为与测试数据中的因素相匹配。

library(randomForest)

traindf <- structure(
    list(
        year = c(2001L, 2004L, 2008L, 2010L, 2004L, 2011L),
        sex = structure(
            c(2L, 2L, 1L, 2L, 2L, 1L),
            .Label = c("female",
                                 "male"),
            class = "factor"
        ),
        age = structure(
            c(1L, 6L, 3L, 6L,
                2L, 3L),
            .Label = c(
                "15-24 years",
                "25-34 years",
                "35-54 years",
                "5-14 years",
                "55-74 years",
                "75+ years"
            ),
            class = "factor"
        ),
        log_population = c(
            14.0462476055718,
            10.0651811415341,
            13.5550389013841,
            10.2665669441479,
            15.5047227728237,
            13.4021140795298
        ),
        log_suicides_per_100k = c(
            2.42657107277504,
            4.03069453514564,
            2.38508631450579,
            4.15261347034608,
            2.88480071284671,
            0.647103242058539
        ),
        log_gdp_per_capita = c(
            7.67786350067821,
            9.13701670755734,
            11.1338150021447,
            9.65117262392164,
            7.95472333449791,
            8.14177220465645
        )
    ),
    row.names = c(7888L, 8465L, 7593L, 8535L,
                                25159L, 9656L),
    class = "data.frame"
)

rf3 <- randomForest(log_suicides_per_100k ~ age+sex+log_gdp_per_capita+log_population+year, data=traindf)

testdf <- data.frame(age='75+ years', sex='male', log_gdp_per_capita=13.082, log_population=9.393, year=2025)
testdf$sex <- factor(testdf$sex, levels=levels(traindf$sex))
testdf$age <- factor(testdf$age, levels=levels(traindf$age))

prediction <- predict(rf3, testdf)
prediction

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