sklearn tsne

t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique commonly used for visualizing high-dimensional data. In scikit-learn (sklearn), you can use the `TSNE` class from the `manifold` module to perform t-SNE analysis.

Here's an example code snippet to demonstrate how to use t-SNE in sklearn:

from sklearn.manifold import TSNE

# Assuming you have your high-dimensional data stored in X
# X.shape = (n_samples, n_features)

# Create an instance of t-SNE
tsne = TSNE(n_components=2, random_state=42)

# Apply t-SNE on your data
X_tsne = tsne.fit_transform(X)

# X_tsne.shape = (n_samples, n_components)

In the above code, `n_components` specifies the number of dimensions in the embedded space (usually set to 2 or 3 for visualization purposes). The `random_state` parameter ensures reproducibility of results.

After applying t-SNE, the transformed data is stored in `X_tsne`, where each sample is represented by its coordinates in the embedded space.

Remember to preprocess your data appropriately before applying t-SNE, as it is sensitive to scaling and may require feature scaling or normalization depending on the characteristics of your data.