python 驗證模型

This is a memo to share what I have learnt in Model Validation (using Python), capturing the learning objectives as well as my personal notes. The course is taught by Kasey Jones from DataCamp.

這是一份備忘錄，分享了我在模型驗證(使用Python)中學到的知識，記錄了學習目標以及我的個人筆記。 該課程由DataCamp的Kasey Jones教授。

A machine learning model needs to go through proper validation in order to ensure optimum model performance on new data.

機器學習模型需要經過適當的驗證，以確保對新數據的最佳模型性能。

I have learnt the following topics:

我已經學習了以下主題：

• Basics of model validation
  模型驗證的基礎
• Accuracy and evaluation metrics
  準確性和評估指標
• Splitting data into train, validation, and test sets
  將數據分為訓練，驗證和測試集

• Validation techniques

  驗證技術

  Cross-validation and LOOCV

  交叉驗證和LOOCV

• tools for creating validated and high performing models
  用于創建經過驗證的高性能模型的工具
• Hyperparameter tuning
  超參數調整

More notes and codes can be found on my GitHub.

在我的GitHub上可以找到更多注釋和代碼。

Overall, I have enjoyed learning this course and would highly recommend it!

總的來說，我很喜歡學習這門課程，并強烈推薦它！

翻譯自: https://medium.com/ai-in-plain-english/model-validation-in-python-ad23c1d215b

python 驗證模型

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