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largest decrease » larger decrease (Expand Search), marked decrease (Expand Search)
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_ largest » _ large (Expand Search)
largest decrease » larger decrease (Expand Search), marked decrease (Expand Search)
values decrease » values increased (Expand Search)
learning data » learning dataset (Expand Search), learning a (Expand Search)
data decrease » rate decreased (Expand Search), a decrease (Expand Search), deaths decreased (Expand Search)
_ largest » _ large (Expand Search)
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List of network connections predictive of individual SoF values, together with their β weights, in decreasing order.
Published 2025“…<p>List of network connections predictive of individual SoF values, together with their β weights, in decreasing order.…”
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Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreasing order by absolute value for the cancer outcomes.
Published 2025“…The features are sorted in decreasing order by absolute value for the cancer outcomes.…”
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The MAE value of the model under raw data.
Published 2025“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”
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Three error values under raw data.
Published 2025“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”
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Comparison of environmental perception time results at different learning rates.
Published 2025Subjects: -
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TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5<sup>th</sup> and 95<sup>th</sup> percentile change points are determined from 500 bootstrap replicate runs....
Published 2025“…<p>TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5<sup>th</sup> and 95<sup>th</sup> percentile change points are determined from 500 bootstrap replicate runs. …”
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Forest plot of ROR values used to assess gender differences in penicillamine-related AEs.
Published 2025Subjects: