机器学习：4.3 模型验证 Model Validation

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Generalization Error

• Approximated by the error on a holdout test dataset, which has never been seen by the model and can be only used once

  • Your midterm exam score

  • The final price of a pending house sale

  • Dataset used in private leaderboard in Kaggle

• Validation dataset:

  • Often a subset of the dataset, not used for model training

  • Can be used multiple times for hyper param tuning

  • “test error” usually refers to error on “validation” dataset

Hold Out Validation

• Split your data into “train” and “valid” sets (often calls “test”)
• Train your model on the train set, use the error on the valid set to approximate the generalization error
• Often randomly select examples as the valid set
  • Typical choices n = 50, 40, 30, 20, 10

Split non I.I.D. data

• Random data splitting may lead to underestimate of generalization error

• Sequential data

  • e.g. house sales, stock prices

  • Valid set should not overlap with train set in time

• Examples are highly clustered

  • e.g. photos of the same person, clips of the same video

  • Split clusters instead of examples

• Highly imbalanced label classes

  • sample more from minor classes

K-fold Cross Validation

• Useful when not sufficient data

• Algorithm:

  • Partition the training data into K parts

  • For i = 1, …, K

    • Use the i-th part as the validation set, the rest for training

  • Report the validation error averaged over K rounds

• Popular choices: K = 5 or 10

Common Mistakes

• If your ML model performance is too good to be true, very likely
  there is a bug, and contaminated valid set is the #1 reason.
• Valid set has duplicated examples from train set
  • Often happens when integrating multiple datasets
    • Scrape images from search engine to evaluate models trained on ImageNet
• Information leaking from train set to valid set
  • Often happens for non I.I.D data
    • use future to predict past, see a person’s face before
• Excessive use of valid set for hyper param tuning is cheating

Summary

• The test data is used once to evaluate your model

• One can hold out a validation set from the training data to estimate the test data

  • You can use valid set multiple times for model selections and hyper
    param tuning

  • Validation data should be close to the test data

  • Improper valid set is a common mistake that lead to over estimate of the model performance