Training set - Validation set - Test set - Development set (dev set)
1. Training set - Validation set - Test set
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Training set: The data you will use to train your model. This will be fed into an algorithm that generates a model. Said model maps inputs to outputs.
你將用來訓練模型的數據。這將被輸入到生成模型的算法中。所述模型將輸入映射到輸出。 -
Validation set: This is smaller than the training set, and is used to evaluate the performance of models with different hyperparameter values. It’s also used to detect overfitting during the training stages.
它小於訓練集，用於評估具有不同超參數值的模型的性能。它也用於在訓練階段檢測過擬合。 -
Test set: This set is used to get an idea of the final performance of a model after hyperparameter tuning. It’s also useful to get an idea of how different models (SVMs, Neural Networks, Random forests…) perform against each other.
使用此集可以瞭解超參數調整後模型的最終性能。瞭解不同模型 (SVMs, Neural Networks, Random forests…) 之間如何相互影響也很有用。
2. development set (dev set)
development set (dev set) = hold out cross validation set
cross validation：交叉驗證
References
https://www.brainstobytes.com/test-training-and-validation-sets/