Σύγκριση μεθόδων

	Ανάλυση Κύριων Συνιστωσών	SHAP (SHapley Additive exPlanations)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2002	2017
Δημιουργός	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Lundberg, S.M. & Lee, S.-I.
Τύπος	Unsupervised dimensionality reduction	Model-explanation method (Shapley-value attribution)
Θεμελιώδης πηγή	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.
Εναλλακτικές ονομασίες	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability
Συναφείς	3	5
Σύνοψη	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).