השוואת שיטות

	k-השכנים הקרובים הרגולרי (Regularized k-Nearest Neighbors)	רגרסיה לוגיסטית עם רגולריזציה
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1967–2000s	1996–2005
הוגה השיטה	Extends Cover & Hart (1967); regularization formulations developed through kernel smoothing literature	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
סוג	Instance-based / lazy learner with regularization	Penalized classification model
מקור מכונן	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
כינויים	regularized kNN, kernel-weighted kNN, distance-regularized nearest neighbors, kNN with regularization	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
קשורות	4	5
תקציר	Regularized k-Nearest Neighbors (kNN) extends the classical nearest-neighbor algorithm by incorporating regularization mechanisms — most commonly kernel-based distance weighting or bandwidth control — that smooth predictions, reduce sensitivity to the choice of k, and lower variance. The result is a more stable and better-calibrated instance-based learner for classification and regression tasks on tabular data.	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.