两个参数一致的R语言KNN模型返回不同结果的问题咨询

Hey there! Let's break down why your two knn() calls with the exact same parameters are producing different predictions, and how to make your results fully reproducible.

What's causing the inconsistency? #

The knn() function from the class package has a hidden random element: tie-breaking. When multiple classes receive an equal number of votes from the k-nearest neighbors (for example, using k=4 and getting 2 votes for Class A, 2 for Class B), the function will randomly select one of the tied classes as the prediction. Even if your training/test data is identical, these random tie-breaks will lead to different prediction outputs across runs.

Even with an odd k value like your k=5, ties can still occur in edge cases—say you have 3 classes and the vote split is 3-1-1 (no tie), but if you end up with a scenario where two classes are tied for the most votes, the randomness kicks in. This is exactly why your two confusion matrices don't match.

How to fix it: Lock in reproducibility with a seed #

The solution is straightforward: set a random seed before calling knn(). This locks the state of R's random number generator, so any tie-breaking steps will produce the exact same result every time you run the code.

Here's how to adjust your code:

# Pick any integer as your seed (123 is a common choice, use whatever you prefer)
set.seed(123)
# First prediction run
impens_test_pred <- knn(train = train_set, test = test_set, cl = train_set_labels$cleavage, k = 5)

# Reuse the same seed before the second run
set.seed(123)
# Second prediction run (will match the first exactly)
impens_test_pred2 <- knn(train = train_set, test = test_set, cl = train_set_labels$cleavage, k = 5)

Now when you generate your confusion matrices with CrossTable(), both outputs will be identical:

CrossTable(x = test_set_labels$cleavage, y = impens_test_pred, prop.chisq=FALSE)
CrossTable(x = test_set_labels$cleavage, y = impens_test_pred2, prop.chisq=FALSE)

Quick extra tips #

• Always set a seed whenever your model includes random components (tie-breaking, train/test splits, cross-validation, etc.)—it's a critical best practice for reproducible research and debugging.
• To minimize ties, using an odd k value can help (since equal vote splits are less likely), but it won't eliminate them entirely. Setting a seed remains the most reliable way to ensure consistent results.

内容的提问来源于stack exchange，提问作者Jesús Muñoz