XGBoost's "Safety Knobs" (How to Stop Overfitting)
4:29
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 ...
1 day ago
XGBoost Machine Learning
SCI绘图：Xgboost-shap模型
0:12
SCI绘图：Xgboost-shap模型
bilibili是小霹雳呀
643 views5 days ago
SHAP : Choisir le meilleur explainer pour vos modèles
7:38
SHAP : Choisir le meilleur explainer pour vos modèles
YouTubeAlphorm
26 views2 weeks ago
Machine Learning Full Course In 10 Hrs | Machine Learning Full Course For Beginners | Simplilearn
9:11:08
Machine Learning Full Course In 10 Hrs | Machine Learning Full Course For Beginners | Simplilearn
YouTubeSimplilearn
1K views2 days ago
Top videos
XGBoost's Safety Knobs #machinelearningalgorithm #machinelearning
1:47
XGBoost's Safety Knobs #machinelearningalgorithm #machinelearning
YouTubeSchovia
3 views9 hours ago
xgboost #machinelearning #mathematics #dataanlysis #datascience #machinelearning
2:23
xgboost #machinelearning #mathematics #dataanlysis #datascience #machinelearning
YouTubeDataMListic
1.1K views3 days ago
Detection of Distributed Denial of Service Attack on EV Charging Station using XGBoost Algorithm
13:00
Detection of Distributed Denial of Service Attack on EV Charging Station using XGBoost Algorithm
YouTubeFoxtrot
15 hours ago
XGBoost Tutorial
Predicting Stock Returns Using XGBoost vs CAPM
6:01
Predicting Stock Returns Using XGBoost vs CAPM
YouTubeReadyProj
3 days ago
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
6:43
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
TikTokarlestaboada
5.1K views1 week ago
Medicost Predictor | AI Medical Insurance Cost Calculator using Machine Learning | Flask Project
6:24
Medicost Predictor | AI Medical Insurance Cost Calculator using Machine Learning | Flask Project
YouTubecodeAj Marketplace
3 days ago