T-SNE

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover t-SNE?

Reduces numeric multivariate data to plotted dimensions, but stops short of stable interpretation because hyperparameters alter projections.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

t-SNE is a nonlinear dimensionality-reduction technique that maps high-dimensional data to a low-dimensional space while emphasizing local neighborhood structure.

t-SNE is a nonlinear dimensionality-reduction technique for projecting complex, multivariate data into a few dimensions. The resulting chart helps reveal patterns, clusters, and potentially anomalous points that are difficult to see across many original features. section 2.4.3

Use t-SNE with these practical cautions:

Treat the plot as an exploratory aid rather than definitive evidence: changing hyperparameters can produce substantially different projections. section 2.4.3
Compare it with PCA, SVD, or UMAP when linear structure, alternative projections, or additional interpretation matter. section 2.4.3

Listing 2.9 Plotting a t-SNE low-dimensional projection of a dataset

from sklearn.manifold import TSNE                           #1
import matplotlib.pyplot as plt                             #2
 
tsne = TSNE(n_components=2, 
            perplexity=30., 
            init="random", 
            learning_rate="auto",
            random_state=42)                                #3
 
X = data[numeric_feats + ordinal_feats].fillna(
            data[numeric_feats + ordinal_feats].mean())     #4
projection_2D = tsne.fit_transform(X)                       #5
 
plt.figure(figsize=(15, 15))
plt.scatter(projection_2D[:, 0], projection_2D[:, 1],
            edgecolor='none', 
            alpha=0.80, 
            s=10) 
plt.show()                                                  #6

A runnable scikit-learn workflow creates a two-dimensional t-SNE projection and plots its points with matplotlib. section 2.4.3

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

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Also explained in

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Data Without Labelst-distributed stochastic neighbor embedding is a nonlinear dimensionality-reduction technique that maps high-dimensional observations to a low-dimensional space while preserving local similarities.

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Related concepts

dimensionality reductionkind of · Dimensionality reduction is the transformation of data into a representation with fewer dimensions while retaining selected information, structure, or relationships.
embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
Kullback–Leibler divergenceuses · Kullback–Leibler divergence is a nonnegative, directed measure of the information lost when one probability distribution approximates another, computed as an expected log ratio under the first distribution.
UMAPalternative to · UMAP, or Uniform Manifold Approximation and Projection, is a nonlinear manifold-learning technique that embeds high-dimensional data in a lower-dimensional space while preserving important neighborhood and structural relationships.
principal component analysisalternative to · Principal component analysis is a linear dimensionality-reduction technique that transforms data into orthogonal components ordered by the variance they capture.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.