在R中构建Confusion matrix遇问题，寻求技术帮助

Hey there! I’ve dealt with similar confusion matrix headaches when working with SVM models in R, so let’s break down how to get this sorted out for you. You mentioned you’ve confirmed your observation counts match, which is a great first step—let’s build on that.

First: Skip the Merge (Simpler Approach) #

You don’t actually need to merge your data to create a confusion matrix. The table() function works directly with your true labels and predicted values, which avoids any merge-related errors (like the truncated Test_Da... in your code snippet). Here’s how to do it:

# Load required package (you already have this)
library(e1071)

# Your existing model and prediction code (cleaned up a bit)
Svm_Model <- svm(
  t.total_hbat ~ t.inbound_calls_tied + t.hbat_on_tickets_closed_by_agent + t.inb_customer_hold_time,
  data = Test_Data,
  kernel = "linear",
  cost = .1,
  scale = FALSE
)
Svm_Prediction <- predict(Svm_Model, Test_Data, type = "class")

# Create confusion matrix directly with true vs predicted values
confusion_matrix <- table(
  Actual_Labels = Test_Data$t.total_hbat,
  Predicted_Labels = Svm_Prediction
)

# Print the result
print(confusion_matrix)

If You Still Want to Merge Data (For Further Analysis) #

If you need the merged dataset for other tasks, make sure you’re completing the merge correctly. Your code cut off at Test_Da..., so let’s fix that and ensure the columns align:

# Convert predictions to a data frame with a clear column name
pred_df <- data.frame(Predicted_HBAT = Svm_Prediction)

# Merge with your test data (cbind works since rows are aligned)
data_final <- cbind(Test_Data, pred_df)

# Now create the confusion matrix from the merged data
confusion_matrix <- table(
  Actual = data_final$t.total_hbat,
  Predicted = data_final$Predicted_HBAT
)

# For a more detailed matrix (with accuracy, precision, etc.), use the caret package
# Install caret first if you haven't: install.packages("caret")
library(caret)
detailed_confusion_matrix <- confusionMatrix(data_final$Predicted_HBAT, data_final$t.total_hbat)
print(detailed_confusion_matrix)

Common Pitfalls to Check #

• Factor vs. Numeric Labels: If your t.total_hbat is numeric, convert it to a factor first (and do the same for predictions) to ensure the confusion matrix groups categories correctly:

  Test_Data$t.total_hbat <- as.factor(Test_Data$t.total_hbat)
  Svm_Prediction <- as.factor(Svm_Prediction)
  

• Column Name Typos: Double-check that you’re referencing the correct column name for your true labels (t.total_hbat)—typos here will throw errors even if row counts match.

内容的提问来源于stack exchange，提问作者M lee