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YouTube龍馬一閃～競馬はロマン予想は閃き～
【2025菊花賞】XGBoost復活～AIモデルが示す本命馬はこれだ～
龍馬一閃ブログ～新AI予想～ https://doragonhorse.blog.fc2.com/ #菊花賞 龍馬一閃指数をベースとした新・AI予想。さて、どうでしょう
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XGBoost Part 1 (of 4): Regression
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Time Series Forecasting with XGBoost - Use python and machine learning to predict energy consumption
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Visual Guide to Gradient Boosted Trees (xgboost)
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人気の動画
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Optimizing MANETs with XGBoost, PSO, and BWOA for QoS and Congestion | Steve Price posted on the topic | LinkedIn
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linkedin.com
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XGBoost Vs LightGBM
yourquant_rick on Instagram: "Time series models, like ARIMA, are a classic framework for analyzing sequential data. They assume stationarity and lack of correlation, which works well for sales forecasting but struggles in finance. Markets are highly non-stationary — factors like inflation, rising costs, and constant shifts in sentiment affect price behavior. Correlation between assets is often temporary and only strong in high-sentiment markets like crypto or tech. I often combine uncorrelated
yourquant_rick on Instagram: "Time series models, like ARIMA, are a classic framework for analyzing sequential data. They assume stationarity and lack of correlation, which works well for sales forecasting but struggles in finance. Markets are highly non-stationary — factors like inflation, rising costs, and constant shifts in sentiment affect price behavior. Correlation between assets is often temporary and only strong in high-sentiment markets like crypto or tech. I often combine uncorrelated
Instagramyourquant_rick
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AWS for Data Science: EC2 vs. SageMaker vs. Lambda - The Ultimate Guide (with Demos)(3/4)
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𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍ENSEMBLE LEARNING – PART 10.3: BOOSTING Boosting is all about learning from mistakes. Instead of training one big model all at once, it builds a sequence of weak learners — usually simple models that are only slightly better than random guessing. But here’s the trick👇 Each new model pays extra attention to the examples the previous models got wrong. It “boosts” the importance of those mistakes so the next learner can correct them. Over tim
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𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍ENSEMBLE LEARNING – PART 10.3: BOOSTING Boosting is all about learning from mistakes. Instead of training one big model all at once, it builds a sequence of weak learners — usually simple models that are only slightly better than random guessing. But here’s the trick👇 Each new model pays extra attention to the examples the previous models got wrong. It “boosts” the importance of those mistakes so the next learner can correct them. Over tim
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視聴回数: 1993 回1 日前