Binary classification task performance.

<p>Each row is associated with a mental state and each outer column with a forecast horizon. For each of the 30 conditions, bar plots show XGBoost and LSTM test predictions. The bar height corresponds to the number of examples with each bar indicating the true class and colors reflecting the p...

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Bibliographic Details
Main Author:	Thierry Jean (20691795) (author)
Other Authors:	Rose Guay Hottin (20691798) (author), Pierre Orban (62056) (author)
Published:	2025
Subjects:	Neuroscience Science Policy Mental Health Biological Sciences not elsewhere classified Information Systems not elsewhere classified thus providing richer results also showed previous research focused prediction targets derived passive sensor data mean absolute error machine learning algorithm g ., calm forecasting mental states extreme gradient boosting digital phenotyping studies clinical rating scales forecast mental states forecast horizon expanded 3 forecast horizons systematically overestimated performance often ordinal nature next week ). evaluating predictive performance 2 predictive tasks 2 learning algorithms xgboost models performed using performance metrics forecast tasks ordinal regression xlink "> term memory small decrease similar levels significant yet seeing things remains unclear popular choices next day lstm models long short continuous regression binary classification best suited 73 %, 551 days
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Summary:	<p>Each row is associated with a mental state and each outer column with a forecast horizon. For each of the 30 conditions, bar plots show XGBoost and LSTM test predictions. The bar height corresponds to the number of examples with each bar indicating the true class and colors reflecting the predicted classes. The BAcc for these XGBoost and LSTM model predictions are shown against the BAcc significance threshold (Bonferroni corrected <i>P</i> < .05) and the corresponding Monte Carlo baseline distribution. n.s., non-significant.</p>

Binary classification task performance.

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