What is t-SNE in Python machine learning?

What is t-SNE in Python machine learning?

t-SNE, or t-distributed Stochastic Neighbor Embedding, is a technique for dimensionality reduction that is particularly well-suited for visualizing high-dimensional datasets.

How does t-SNE work?

t-SNE works by converting similarities between data points into joint probabilities and then minimizing the divergence between these probabilities in high and low dimensions.

Can you show me a simple example of using t-SNE in Python?

Sure! Here's a basic example using the sklearn library:

from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

# Sample data
X = [[1, 2], [2, 3], [3, 4], [5, 6]]

# Applying t-SNE
model = TSNE(n_components=2)
X_embedded = model.fit_transform(X)

# Plotting
plt.scatter(X_embedded[:, 0], X_embedded[:, 1])
plt.show()

What are the limitations of t-SNE?

Some limitations of t-SNE include its computational intensity, difficulty in interpreting the results, and sensitivity to hyperparameters.