Dimensionality reduction simplifies datasets by reducing the number of features while preserving essential information. It improves model performance and visualization.
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_reduced = pca.fit_transform(X)
LDA is used for supervised dimensionality reduction.
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda = LinearDiscriminantAnalysis(n_components=1)
X_lda = lda.fit_transform(X, y)
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2)
X_tsne = tsne.fit_transform(X)
Dimensionality reduction is crucial when working with high-dimensional datasets, improving both speed and efficiency of models.
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