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Articles by AdaBoost Regressor

Reimagining Classic Strategies (Part VI): Multiple Time-Frame Analysis

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By Random ForestRegressor, Gradient BoostingRegressor, AdaBoost Regressor, KNeighbors Regressor
| mql5.com
MetaTrader 5 — Examples | 20 August 2024, 14:26 19 0 There are potentially infinite ways in which a modern investor can integrate Artificial Intelligence (AI) to enhance their trading decisions. Unfortunately, it is unlikely that you can evaluate all these strategies before deciding which strategy to trust with your hard-earned capital. In this series of articles, we will explore trading strategies to assess whether we can improve the strategy with AI.
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Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR

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By Gradient BoostingRegressor, Random ForestRegressor, AdaBoost Regressor, KNeighbors Regressor
| mql5.com
MetaTrader 5 — Examples | 16 August 2024, 16:51 55 0 There are innumerable ways we can integrate AI into our trading strategies, but unfortunately, we cannot evaluate each one before we decide which one to trust with our capital. Today, we revisit a popular trading strategy of multiple symbol analysis to determine if we can improve the strategy using AI.
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Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes

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By Random ForestRegressor, Gradient BoostingRegressor, AdaBoost Regressor, Standard Deviations
| mql5.com
MetaTrader 5 — Examples | 13 August 2024, 15:04 368 0 In our previous article, we discussed a potential S&P 500 trading strategy that would rely on us using a selection of stocks that held high weights inside the index. In today's article, we're going to look at an alternative approach of trading the S&P 500 using the Yield of Treasury Notes.
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阿里云天池大赛赛题（机器学习）--工业蒸汽量预测（完整代码）_工业蒸汽量预测代码_全栈O-Jay的博客-CSDN博客

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By AdaBoost Regressor, Gradient BoostingRegressor, Extra TreesRegressor
| CSDN
火力发电就是燃料燃烧加热水生成蒸汽，蒸汽产生的压力推动汽轮机旋转，进而带动电机旋转，产生电能。其中一系列的能量转化中，影响发电效率的核心是锅炉的燃烧效率，即加热水产生的蒸汽量。而影响锅炉燃烧效率的因素很多，包括锅炉床温、床压、炉膛温度、压力等等。 这个赛题的目标就是给一堆锅炉传感器采集的数据（38个特征变量），然后用训练好的模型预测出蒸汽量。因为预测值为连续型数值变量，且给定的数据都带有标签，故此问题是典型的回归预测问题。 典型的回归预测模型使用的算法包括：线性回归，岭回归，LASSO回归，决策树回归，梯度提升树回归。 一个典型的机器学习实战算法基本包括 1) 数据处理，2) 特征选取、优化，和 3) 模型选取、验证、优化。 因为 “数据和特征决定了机器学习的上限，而模型和算法知识逼近这个上限而已。” 所以在解决一个机器学习问题时大部分时间都会花在数据处理和特征优化上。 大家最好在jupyter notebook上一段一段地跑下面的代码，加深理解。 机器学习的基本知识可以康康我的其他文章哦 好康的。 导入包 import warnings...
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Laptop Price Prediction in Machine Learning

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By Gradient BoostingRegressor, Raghav Agrawal, AdaBoost Regressor
| Analytics Vidhya
This article was published as a part of the Data Science Blogathon. that can make a future prediction based on past data. If you are a data science enthusiast or a practitioner then this article will help build your own end-to-end machine learning project from scratch. There are various steps involved in building a machine learning project but not all the steps are mandatory to use in a single project, and it all depends on the data.
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