h2o.kmeans()是否基于欧氏距离进行预测？

Hey there! Let's tackle your H2O K-Means questions one by one:

1. Does h2o.kmeans() use Euclidean distance for predictions? #

Absolutely, by default! H2O's K-Means algorithm relies on Euclidean distance as the standard metric to assign new observations to the closest cluster during prediction.

That said, you're not locked into Euclidean distance. You can swap it out using the distance parameter when building your model. For example, if you want to use Manhattan (L1) distance instead, your code would look like this:

kmeans_model <- h2o.kmeans(training_frame = my_train_data, k = 5, distance = "manhattan")

Other supported metrics include cosine and Pearson correlation too.

One quick note: If you had let H2O handle standardization via standardization = TRUE, it would have scaled your data internally before calculating distances. But since you already standardized your data with R's scale(), make sure to set standardization = FALSE in h2o.kmeans() to avoid double-scaling your data (which would throw off your centroid values).

2. Understanding your standardized centroids #

Since you pre-scaled your data with scale() (which centers each feature to a mean of 0 and scales to a standard deviation of 1), every value in your centroid table tells you how far that cluster's average for a feature is from the global mean of your original training data, measured in standard deviation units.

Let's use your Cluster 1 centroids as an example:

• The value -0.646544 for X1 means that, on average, observations in this cluster have an X1 score that's roughly 0.65 standard deviations below the overall mean of X1 in your training set.
• The large negative values for features like X5 through X22 (e.g., -1.618 for X5, -2.221 for X11) show that this cluster is strongly defined by below-average values across those features.

Critical best practice for new predictions #

When you use this model to cluster new data, you must apply the exact same scaling transformation that you used on your training data. That means using the mean and standard deviation values from your original training set (not the new data's stats) to scale the new observations.

Here's how you can save those scaling parameters and reuse them:

# Save scaling parameters when preprocessing training data
scaled_train <- scale(my_training_data)
train_centers <- attr(scaled_train, "scaled:center")
train_scales <- attr(scaled_train, "scaled:scale")

# Apply the same scaling to new data
scaled_new_data <- scale(new_data, center = train_centers, scale = train_scales)

If you skip this step, your new data will be in a different scale than the centroids, leading to wrong cluster assignments.

内容的提问来源于stack exchange，提问作者soniCYouth