1樓：武宗海山
一般來說，訓練集佔據了資料集的majority（例如百分之80），訓練集用於確定模型的basic引數。而驗證集（假設佔據10%）則是在訓練模型的過程中不斷調整basic引數，也就是常說的「調參」。當模型的引數最終確定後，停止訓練，採用測試集（10%）來評估模型的泛化效能。
2樓：和煦
for each epoch
for each training data instance
propagate error through the network
adjust the weights
calculate the accuracy over training data
for each validation data instance
calculate the accuracy over the validation data
if the threshold validation accuracy is met
exit training
else
continue training
Once you're finished training, then you run against your testing set and verify that the accuracy is sufficient.
Training Set:this data set is used to adjust the weights on the neural network.
Validation Set:this data set is used to minimize overfitting. You're not adjusting the weights of the network with this data set, you're just verifying that any increase in accuracy over the training data set actually yields an increase in accuracy over a data set that has not been shown to the network before, or at least the network hasn't trained on it (i.
e. validation data set). If the accuracy over the training data set increases, but the accuracy over then validation data set stays the same or decreases, then you're overfitting your neural network and you should stop training.
Testing Set:this data set is used only for testing the final solution in order to confirm the actual predictive power of the network.
Validating set is used in the process of training. Testing set is not. The Testing set allows
1)to see if the training set was enough and 2)whether the validation set did the job of preventing overfitting.
3樓：shirley
Training set: A set of examples used for learning, which is to fit the parameters [i.e.
, weights] of the classifier.
Validation set:A set of examples used to tune the parameters [i.e.,
architecture, not weights] of a classifier, for example to choose the
number of hidden units in a neural network.
Test set: A set of examples used only to assess the performance [generalization] of a fully specified classifier.
4樓：
訓練集（train set）：用於訓練模型以及確定模型權重。
驗證集（validation set）：用於確定網路結構以及調整模型的超引數。
測試集（test set）：用於檢驗模型的泛化能力。
如何有效的評估模型？
5樓：小宇
1.a set of methods to automatically detect patterns in data and use them to
Predict future data
Make decisions
input-output pairs:
N is the number of training examples
3.Each training input is a D-dimensional vector
Stored in an design matrix
Each dimension corresponds to a 「feature」
Each training output can:
Belong to a finite set, ∈ classification or pattern recognition
Classification with C=2 is often called 「detection」
Be a real value regression
6樓：千佛山彭于晏
原來測試集除了評估準確性之外，還有驗證模型的推廣能力的作用啊，因為不確定驗證集有無足夠的泛化能力，這也是測試集設立的原因為之一啊
LSTM訓練集和驗證集的loss曲線為什麼會是這樣？
醉笑陪公看落花 前面回答已經很好了，再補充一點.題主提出這樣的問題說明對機器學習的一些基本知識還沒有乙個系統的學習.但與此同時能訓練出乙個不錯的LSTM網路，說明題主的應用實踐能力很強.那我們可以從應用上手，改變訓練集大小 改變模型複雜度，然後再看loss曲線的變化，這樣更有助於理解val loss...
在深度學習中，如果訓練集和測試集的範圍不一致，該如何進行歸一化或者標準化處理？
王華 如果你的測試集和訓練集不能動。那就乾脆縮放吧，都縮放到0 1之間。雖說，前面有說訓練集和測試集要同分布，說的很堅決，必須要。我是不同意這種說法的，分布這種本身概念就很模糊。打過各種比賽，神經網路需要效果優先的，你可以試試縮放法。 華矩數診台 你可以使用歸一化，訓練集0 10，歸一化不就是X X...
NLP中建立的訓練集詞表 字典 的目的是什麼呢？和已經預訓練好的詞向量之間有什麼關係？
阿良 首先明確題主的問題 為什麼我們要構建訓練集的詞表 字典 這個詞表和已經訓練好的詞向量之間的關係是什麼？為什麼要構建訓練集的詞表？構建詞表的目的是對字串表示的詞進行向量化。因為當前的自然語言處理模型都是基於統計機器學習，只能在數學上進行各種計算，這樣就勢必要求將字串表示的文字數位化。假設我們有一...