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YouTubeSchovia
XGBoost's "Safety Knobs" (How to Stop Overfitting)
What makes XGBoost "Extreme"? It's not just the speed—it's the control. In this video, we open up the "equations engine" to reveal how Lambda and Gamma keep your model modest and robust. In this video: We explore the final "Control" mechanisms of XGBoost: Pruning and Regularization. While the Learning Rate controls the speed of learning ...
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El estudio probó y comparó tres enfoques de modelos de ensamble—bagging (RF), boosting (XGBoost) y stacking (gcForest)—utilizando 25 factores ambientales y topográficos. El análisis determinó que la altitud, la distancia a los ríos y las residencias, y el uso del suelo son los elementos más influyentes en la ocurrencia de deslizamientos. #randomForest #XGBoost #boosting #stacking
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El estudio probó y comparó tres enfoques de modelos de ensamble—bagging (RF), boosting (XGBoost) y stacking (gcForest)—utilizando 25 factores ambientales y topográficos. El análisis determinó que la altitud, la distancia a los ríos y las residencias, y el uso del suelo son los elementos más influyentes en la ocurrencia de deslizamientos. #randomForest #XGBoost #boosting #stacking
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XGBoost's Safety Knobs #machinelearningalgorithm #machinelearning
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