השוואת שיטות

	רגרסיית הובר	LightGBM
תחום	סטטיסטיקה	למידת מכונה
משפחה	Regression model	Machine learning
שנת המקור	1964	2017
הוגה השיטה	Peter J. Huber	Ke, G. et al. (Microsoft)
סוג	Robust linear regression (M-estimation)	Gradient boosting decision tree ensemble
מקור מכונן	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.
כינויים	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
קשורות	5	5
תקציר	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.