 Thus , it is crucial for machine learning models to account for self-tracking artifacts like skipped self-tracking. In this abstract , we propose and evaluate a hierarchical, generative model for predicting next cycle length based on previously tracked cycle lengths that accounts explicitly for the possibility of users URLetting to track their period.
<meaning-changed>  Thus , it is crucial for machine learning models to account for self-tracking artifacts like skipped self-tracking. In this abstract , we propose and evaluate a hierarchical, generative model for predicting next cycle length based on previously tracked cycle lengths that accounts explicitly for the possibility of users URLetting to track their period.
 Thus , it is crucial for machine learning models to account for self-tracking artifacts . By taking a machine learning approach to modeling self-tracked cycle lengths, we can both make more informed predictions and learn the underlying structure of the observed data. In this work , we propose and evaluate a hierarchical, generative model for predicting next cycle length based on previously tracked cycle lengths that accounts explicitly for the possibility of users URLetting to track their period.