Linganisha mbinu

	Uchanganuzi wa Vipengele Vikuu	SHAP (SHapley Additive exPlanations)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2002	2017
Mwanzilishi	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Lundberg, S.M. & Lee, S.-I.
Aina	Unsupervised dimensionality reduction	Model-explanation method (Shapley-value attribution)
Chanzo asilia	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.
Majina mbadala	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability
Zinazohusiana	3	5
Muhtasari	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).