Predicting Stock Returns Using XGBoost vs CAPM
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YouTubeReadyProj
Predicting Stock Returns Using XGBoost vs CAPM
A complete financial machine learning project that teaches you Predicting Stock Returns Using XGBoost vs CAPM using real stock market data. You will learn feature engineering, time-series modeling, CAPM risk analysis, XGBoost tuning, and financial interpretation — ideal for students building AI-for-Finance, quant analytics, and algorithmic ...
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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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