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Can t-SNE be used for classification?
So essentially it is mainly a data exploration and visualization technique. But t-SNE can be used in the process of classification and clustering by using its output as the input feature for other classification algorithms.
Is t-SNE only for visualization?
While t-SNE is a dimensionality reduction technique, it is mostly used for visualization and not data pre-processing (like you might with PCA). It does this in a non-linear and local way, so different regions of data could be transformed differently. t-SNE has a hyper-parameter called perplexity.
What is t-SNE in AI?
t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.
Can a tSNE setting be used for prediction?
I found that tsne separates my binary classification setting quite well. However, tsne cannot really be used for prediction, as in predict (tsne, newdata=test) which can be done for PCA. What is the best approach here? Should I combine my train and test set (i.e., rbind) and run tsne on the whole data set?
How to use predictive modeling to predict t-SNE?
Use the model trained in step 2 to first predict the t-SNE embedding of a test data point and then assign it to a class using kNN. This ensures that there is no data leakage between your training and test set. I believe this approach is a hacky way of bringing t-SNE into the binary classification picture.
What can t-SNE be used for in data science?
Do note that t-SNE was mainly intended for visualization of high dimensional data points and not to extract good features for a classification model.
When do you use a t-SNE matrix?
You can get that matrix and apply it to a new set of data with the same result. That’s helpful when you need to try to reduce your feature list and reuse matrix created from train data. t-SNE is mostly used to understand high-dimensional data and project it into low-dimensional space (like 2D or 3D).