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Explore and run machine learning code with Kaggle Notebooks | Using data from TalkingData AdTracking Fraud Detection Challenge

零、引言 當我們在訓練模型時，並不是訓練越久越好，雖然對訓練模型可以有好的擬合度，但對於「非訓練集」的資料卻不是這麼回事，訓練過度就有可能造成「過擬合」(overfitting)。今天就想來談一談ov...

不是資工系能找到機器學習工作嗎？可以！2021 年我從商管學士跨領域挑戰了機器學習職缺，投了 10 家公司的履歷、經歷了 9 場面試、最終拿到 1 間錄取。本文寫下我的面試歷程、機器學習面試題、以及準備方法與學習資源，希望為大家增加信心：非本科系是有機會拿到機器學習工作錄取的！

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Learn about the structural differences, feature methods, and trade-offs between XGBoost and LightGBM in machine learning.

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缺失值是现实数据集中的常见问题，处理缺失值是数据预处理的关键步骤。缺失值可能由于各种原因而发生，例如数据的结构和质量、数据输入错误、传输过程中的数据丢失或不完整的数据收集。这些缺失的值可能会影响机器…

由於我不知道在哪個網站上看到說kaggle裡面高分群的人多是用雙模型，於是我也上網去找看看雙模型怎麼...

Mastering tabular data on Kaggle requires knowing which machine learning models deliver the best performance. Explore 7 popular models like XGBoost, LightGBM, CatBoost, and more. Understand their strengths, key features, and when to use each to improve your Kaggle competition results.

若是有參加過機器學習競賽的人應該都會聽過 XGBoost 這個橫掃各種競賽的演算法，其實後來也有衍生出 LightGBM 和 Cat boost 這兩種演算法可以使用，只是比較少人聽過，又鑒於這方面資料似乎比較少，就自己吃了一些學習資源來介紹，順便推動自己的學習，希望才疏學淺的本...

從在學校以來就很少碰到決策樹的演算法，決策樹演算法相較於深度學習有較好的可解釋性、且需要調整的參數較少，這也是為什麼我想要研究決策樹演算法的關係。上一篇《決策樹是什麼》簡單描述了決策樹的演算方式，這偏則是要介紹大名鼎鼎的XGBOOST《XGBoost: A Scalable…

XGboost全名為eXtreme Gradient…

My colleague Chris wrote a post about the popular video playback client VLC being pulled from the App Store following objections from a developer who worked on the project that it was in breach of the GNU Public License. [The app has now been released on Cydia for jailbroken devices.] This unpopular move had provoked strong reactions throughout the Mac blogging world, and Chris made a strong case for the prosecution in his post. However, every story always has two sides (and somewhere between them lies the truth). I'm here today to present the case for the defense: why Rémi Denis-Courmont was absolutely in the right to do what he did. However, to do so, I'm going to have to lay out some open source history to give structure to my arguments. Please follow me on.

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