Dimensionality Reduction
Learn various dimensionality reduction techniques for data analysis. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
Dimensionality Reduction Techniques
Dimensionality reduction helps reduce the number of features while preserving important information. Common techniques include PCA, t-SNE, and UMAP.. This is an essential concept that every AI/ML developer must understand thoroughly. In professional development environments, getting this right can mean the difference between code that works reliably and code that breaks in production. The following sections break this down into clear, digestible pieces with practical examples you can try immediately
Comparison of Techniques
from sklearn.manifold import TSNE
import umap
# PCA (already implemented above)
# t-SNE
tsne = TSNE(n_components=2, random_state=42)
X_tsne = tsne.fit_transform(X)
# UMAP
reducer = umap.UMAP(random_state=42)
X_umap = reducer.fit_transform(X)
# Compare visualizations
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# PCA
axes[0].scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis')
axes[0].set_title('PCA')
# t-SNE
axes[1].scatter(X_tsne[:, 0], X_tsne[:, 1], c=y, cmap='viridis')
axes[1].set_title('t-SNE')
# UMAP
axes[2].scatter(X_umap[:, 0], X_umap[:, 1], c=y, cmap='viridis')
axes[2].set_title('UMAP')
plt.tight_layout()
plt.show()