python 驗(yàn)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(yàn)證(使用Python)中學(xué)到的知識(shí)，記錄了學(xué)習(xí)目標(biāo)以及我的個(gè)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(jī)器學(xué)習(xí)模型需要經(jīng)過適當(dāng)?shù)尿?yàn)證，以確保對(duì)新數(shù)據(jù)的最佳模型性能。

I have learnt the following topics:

我已經(jīng)學(xué)習(xí)了以下主題：

• Basics of model validation
  模型驗(yàn)證的基礎(chǔ)
• Accuracy and evaluation metrics
  準(zhǔn)確性和評(píng)估指標(biāo)
• Splitting data into train, validation, and test sets
  將數(shù)據(jù)分為訓(xùn)練，驗(yàn)證和測(cè)試集

• Validation techniques

  驗(yàn)證技術(shù)

  Cross-validation and LOOCV

  交叉驗(yàn)證和LOOCV

• tools for creating validated and high performing models
  用于創(chuàng)建經(jīng)過驗(yàn)證的高性能模型的工具
• Hyperparameter tuning
  超參數(shù)調(diào)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(xué)習(xí)這門課程，并強(qiáng)烈推薦它！

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

python 驗(yàn)證模型

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