在r产生na/nan/inf中的knn功能在外国功能呼叫中（arg 6）错误

发布于 2025-01-24 13:22:56 字数 1015 浏览 3 评论 0原文

我正在研究一个项目，我需要使用R构建KNN模型。教授提供了一篇文章，上面有分步说明（链接到Artist ）和一些数据集可供选择（链接到我正在使用的数据）。我被困在第3步中（从培训数据中创建模型）。

这是我的代码：

data <- read.delim("data.txt", header = TRUE, sep = "\t", dec = ".") 
set.seed(2)
part <- sample(2, nrow(data), replace = TRUE, prob = c(0.65, 0.35))
training_data <- data[part==1,]
testing_data <- data[part==2,]
outcome <- training_data[,2]
model <- knn(train = training_data, test = testing_data, cl = outcome, k=10)

这是我收到的错误消息：

我检查了一下，发现triagn_data，testing_data和结果看起来都是正确的，问题似乎仅与KNN模型有关。

原文

I'm working on a project where I need to construct a knn model using R. The professor provided an article with step-by-step instructions (link to article) and some datasets to choose from (link to the data I'm using). I'm getting stuck on step 3 (creating the model from the training data).

Here's my code:

data <- read.delim("data.txt", header = TRUE, sep = "\t", dec = ".") 
set.seed(2)
part <- sample(2, nrow(data), replace = TRUE, prob = c(0.65, 0.35))
training_data <- data[part==1,]
testing_data <- data[part==2,]
outcome <- training_data[,2]
model <- knn(train = training_data, test = testing_data, cl = outcome, k=10)

Here's the error message I'm getting:

I checked and found that training_data, testing_data, and outcome all look correct, the issue seems to only be with the knn model.

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黑白记忆 2025-01-31 13:22:56

问题在于您的数据和您正在使用的knn功能；它无法处理角色或因素变量，

我们可以强迫这样做这样的工作：

library(tidyverse)

data <- data %>% 
            mutate(Seeded = as.numeric(as.factor(Seeded))-1) %>%
            mutate(Season = as.numeric(as.factor(Season)))

这通常是一个坏主意，因为季节不是自然而然的。一种更好的方法是将其视为一组假人。

请参阅此链接以获取示例：

r-从分类转换为knn

The issue is with your data and the knn function you are using; it can't handle characters or factor variable

We can force this to work doing something like this first:

library(tidyverse)

data <- data %>% 
            mutate(Seeded = as.numeric(as.factor(Seeded))-1) %>%
            mutate(Season = as.numeric(as.factor(Season)))

But this is a bad idea in general, since Season is not ordered naturally. A better approach would be to instead treat it as a set of dummies.

See this link for examples:

R - convert from categorical to numeric for KNN

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