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influence of adding last index - time series data
1. currently - Im generating features using 'process windows' and extract aggregate as sub process. The extracted features are given to train my machine learning model.
2. Ive noticed - by choosing yes for 'adding last index to windows attribute' in the parameter of process windows operator, improves the performance of the model drastically. i.e. from 67% accuracy to 97% accuracy. Ive noticed the difference is adding one extra column in the generated features column. I' m not able to get this point of how this influence the performance of the model.
Is it correct to consider this performance of 97% & can anyone help to understand the role of adding last index. thanks.