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how to use predict and forecast

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DARLINGTON ETAJE
DARLINGTON ETAJE on 21 Oct 2019
Commented: DARLINGTON ETAJE on 21 Oct 2019
Hello Family,
I am trying to predict the class of future dataset: Unfortunately my code is completely misclassifying one class meaning there is a problem with the code.
This is the dataset:
https://drive.google.com/file/d/1tQdZ5XXQAECN6eahzhkt5xLpZA9eOyFk/view?usp=sharing
Here is the code I am currently using:
%D be the dataset
Data=D(1:500,1:end-1);
Data2=D(501:1000,1:end-1);
class_labels=D(1:500,6);
Data2labels=D(501:1000,6);
classification_model = fitcensemble(Data,'class_labels~var1+var2+var3+var4+var5')
cv = cvpartition(classification_model.NumObservations, 'HoldOut', 0.2);
cross_validated_model = crossval(classification_model,'cvpartition',cv);
Predictions_1 = predict(cross_validated_model.Trained{1},Data(test(cv),1:end-1));
Predictions_5 = predict(cross_validated_model{1}.Trained,Data2);
training_data = Data(training(cv),:);
labels = unique(class_labels);
Y = ismember(training_data.class_labels,labels{1});
Results_1 = confusionmat(cross_validated_model.Y(test(cv)),Predictions_1);
Results_5 = confusionmat(Data2labels,Predictions_5);
  2 Comments
John D'Errico
John D'Errico on 21 Oct 2019
It does not mean there is a problem with the code. There could easily be a problem with the data, such that this particular set of data will never be able to predict some events correctly, no matter what model you use. It is not even that difficult to create such a dataset.
DARLINGTON ETAJE
DARLINGTON ETAJE on 21 Oct 2019
Hello John,
Thanks for your comment. That is not the case here. There is a 95 percent accuracy when using the dataset as one..meaning using holdout to divide the data into training set and testing set. But when predicting the future, the story changes....I am positive it's a problem with the code...

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