I am using machine learning classfier to predict my test accuracy. The classfier uses hold out to take data in random. How to I get the train accuracy using the code?

6 次查看（过去 30 天）
Atik Faysal
Atik Faysal 2020-4-15
回答： Omega 2024-12-5
%% -------------- Building Classifier ----------------------------
classification_model=fitcnb(data,'class_labels~PC1+PC2');
%% -------------- Test and Train sets ----------------------------
cv=cvpartition(classification_model.NumObservations,'HoldOut', 0.3);
cross_validated_model=crossval(classification_model,'cvpartition',cv);
%% -------------- Making Predictions for Test sets ---------------
predictions=predict(cross_validated_model.Trained{1},data(test(cv),1:end-1));
%% -------------- Analyzing the predictions ---------------------
results=confusionmat(cross_validated_model.Y(test(cv)),predictions)

请先登录，再回答此问题。

回答（1 个）

Omega
Omega 2024-12-5
Hi Atik,
To obtain the training accuracy, first, you need to extract the training data indices using the "training(cv)" MATLAB function and make predictions on it. Once you have the "trainPredictions," you can calculate the training accuracy by comparing it with the actual "trainLabels."
If you have further questions, please feel free to reach out to me by adding a comment.

类别

在 Help Center 和 File Exchange 中查找有关 Classification 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by