kaggle _Titanic: Machine Learning from Disaster
A Data Science Framework: To Achieve 99% Accuracy
https://www.kaggle.com/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy/notebook
額，總共花了2天時間才把上面這個優秀回答運行完，前面還算看得懂，如何清理數據，和畫圖看聯系
但是後面的數據處理，使用各種模型，不知道原理是什麽，後面還得花點時間補一下，現在這裏記錄一下
疑問匯總：
第一問，第21行，左邊這些各種分類器不熟悉數學原理，以及代碼的實現？？
第2問，第27行對樹模型的操作不是很知悉？？
第3問，這些決策樹，怎麽畫的，怎麽看這些樹形圖？
第4問，後面的各種模型validation也要再看一遍，如何用SKlearn實現的？
Hard Voting 與 Soft Voting 的對比 https://www.cnblogs.com/volcao/p/9483026.html
kaggle _Titanic: Machine Learning from Disaster
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運行 清理 畫圖 man enc acc inf -h ref A Data Science Framework: To Achieve 99% Accuracy https://www.kaggle.com/ldfreeman3/a-data-science-framew
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