Introduction

The most important thing to understand about t-SNE is that it is usually an embedding algorithm for a fixed dataset, not a predictive model with a normal predict() method. In R, packages such as Rtsne are excellent for visualizing high-dimensional data, but they do not naturally provide out-of-sample predictions for new observations the way PCA or a supervised classifier would.

Why Standard t-SNE Does Not "Predict"

t-SNE builds a low-dimensional map by optimizing pairwise relationships among the points it sees during training. The embedding depends on the whole dataset, not on a simple transformation matrix that can be applied to new rows later.

That is why code like this creates an embedding but does not give you a reusable projection object with standard prediction support:

1library(Rtsne)
2
3x <- as.matrix(iris[, 1:4])
4set.seed(42)
5fit <- Rtsne(x, dims = 2, perplexity = 30)
6plot(fit$Y, col = as.integer(iris$Species), pch = 19)

fit$Y contains the two-dimensional coordinates for the data that was fit. It does not mean you can take a brand new row and ask predict(fit, newdata).

What to Do with New Data

If you need to place new observations into a visualization, the simplest honest answer is usually to rerun the embedding using the combined old and new dataset. That keeps the geometry internally consistent, though it can move previously embedded points as well.

If stable out-of-sample projection matters, t-SNE may not be the right tool. A method such as PCA supports exactly that workflow:

1x <- as.matrix(iris[, 1:4])
2pca <- prcomp(x, center = TRUE, scale. = TRUE)
3
4new_points <- matrix(c(5.1, 3.5, 1.4, 0.2,
5                       6.5, 3.0, 5.2, 2.0),
6                     nrow = 2, byrow = TRUE)
7
8projected <- predict(pca, newdata = new_points)
9print(projected[, 1:2])

PCA is linear and easier to project forward. t-SNE is designed for neighborhood-preserving visualization, not clean deployment-time inference.

If the Goal Is Classification, Do Not Use t-SNE as the Model

Another common misunderstanding is trying to run a classifier on a t-SNE map and then use the two-dimensional coordinates for production predictions. That usually creates unstable pipelines because the t-SNE embedding can change with parameters, random seeds, and dataset composition.

If your real task is prediction, train the predictive model on the original features or on a projection method that supports out-of-sample transforms. You can still use t-SNE for exploratory visualization.

fit <- glm(Species == "setosa" ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
           data = transform(iris, Species = as.character(Species)),
           family = binomial())

The principle is simple: use t-SNE to look at structure, not to serve as the production feature mapping unless you have a specialized parametric implementation that explicitly supports that workflow.

Reasonable Alternatives in R

Depending on the goal, better options include:

• PCA when you need a stable projection and predict() support
• UMAP implementations that support transform-style workflows more naturally
• supervised models trained directly on the original variables
• rerunning t-SNE on the combined dataset when the task is purely visualization

The right answer depends on whether you are visualizing, classifying, clustering, or deploying a model to score new data later.

Common Pitfalls

• Expecting Rtsne to expose a normal out-of-sample predict() method.
• Treating t-SNE coordinates as stable production features.
• Comparing t-SNE maps generated with different seeds and parameters as if they were identical spaces.
• Using t-SNE for supervised prediction when the real need is a classifier.
• Forgetting that adding new points can change the original embedding if you rerun it.

Summary

• Standard t-SNE in R is mainly for visualization of the fitted dataset.
• 'Rtsne produces embedded coordinates, not a reusable prediction transform.'
• For new data, rerun t-SNE on combined data or choose a method with real projection support.
• Use PCA or a direct predictive model when out-of-sample scoring matters.
• Treat t-SNE as an exploratory tool unless you intentionally adopt a parametric alternative.