How deeply does Cyber Threat Hunting cover principal component analysis?

Explains the definition and workflow through scaling, variance plotting, transformation, and visualization without deriving PCA mathematically.

Principal component analysis

As explained in Cyber Threat Hunting

Principal component analysis is a dimensionality-reduction method that projects data onto orthogonal components capturing decreasing amounts of variation.

Principal component analysis (PCA) reduces dimensionality while preserving as much variance as possible. It combines correlated features into fewer, independent principal components that retain the most important information. section 9.2.5

Apply PCA through a repeatable preparation and selection workflow:

Split labeled data into training and testing subsets, beginning with an 80–20 split, and check class balance. section 9.2.5
Scale training features to mean zero and standard deviation one with StandardScaler before applying PCA. section 9.2.5

Four-component projection

Setting PCA(n_components=4) transforms X_train_scaled into X_train_pca with columns PC1 through PC4; PC1 and PC2 or PC3 and PC4 can then be plotted by class.

How Cyber Threat Hunting covers it

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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.
covariance matrixuses · A covariance matrix is a square matrix whose diagonal entries are variances and whose off-diagonal entries are pairwise covariances among variables.
varianceuses · Variance is the expected squared deviation of a random variable from its mean, measuring the spread of its distribution.
principal componentproduces · A principal component is an orthogonal direction in transformed data space that captures a ranked amount of variation in the data.
embeddinguses · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.

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