R Random Forest Error - Type of Predictors in New Data Do Not Match

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

Here's some code that works. Because you haven't included all your code, there is a risk that it will not work for you. The factors and levels need to match up so this is the key thing to get right. The factors and levels in the training data are copied and set to match those in the test data.

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

#3.200609 

Error: predictors in new data do not match that of the training data when using raster attribute table (RAT)

The factor RasterLayer (Attribute Layer) seems to be (or be handled like) an ordered factor. So you just have to train the model with an ordered vector. You can achieve this changing one line:

iris.x$Cat<- ordered(as.character(iris.x$Cat), levels = c("Low", "High"))

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