logistic regression model predicting the receipt of nat  (STATA Corporation)

Journal: Earth's Future

Article Title: The Nonlinear and Distinct Responses of Ocean Heat Content and Anthropogenic Carbon to Ice Sheet Freshwater Discharge in a Warming Climate

doi: 10.1029/2024EF004475

Figure Lengend Snippet: (a) Normalized Root Mean Square Error (nRMSE) values between the Gaussian Predictor Response (GPR) predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .

Article Snippet: Here, we identify the different driving factors in the FW linear and nonlinear OHC ANTH and C ANTH responses using a predictive, Gaussian Process Regression (GPR) model in MATLAB's Regression Learner toolbox.

Techniques: Generated, Control

Journal: Patterns

Article Title: Connectome-based machine learning models are vulnerable to subtle data manipulations

doi: 10.1016/j.patter.2023.100756

Figure Lengend Snippet: Main pipeline of performance enhancement attacks This example is shown for prediction of IQ in the HCP dataset with resting-state connectomes and rCPM. The original dataset results in a prediction performance of r = 0.18 between measured and predicted IQ. Enhancement patterns (mean enhancement pattern shown) are added to the original connectome proportional to each participant’s Z -scored IQ. For the sake of visualization, we multiplied the enhancement patterns by 120, 80, and 40, or else they would be too small to see. The corresponding enhanced connectomes maintain average correlations of r ≈ 0.99 with the original connectomes, but the prediction performance is greatly enhanced. The networks labeled on the connectomes are as follows: MF, medial-frontal; FP, fronto-parietal; DMN, default mode; MOT, motor; VI, visual I; VII, visual II; VAs, visual association; SAL, salience; SC, subcortical; and CBL, cerebellum. ,

Article Snippet: For all baseline regression models, we trained ridge-regression connectome-based predictive models (rCPM) in MATLAB (The MathWorks) with 10-fold cross-validation and a nested 10-fold cross-validation to select the L 2 regularization parameter, λ .

Techniques: Labeling

Journal: Patterns

Article Title: Connectome-based machine learning models are vulnerable to subtle data manipulations

doi: 10.1016/j.patter.2023.100756

Figure Lengend Snippet: Performance enhancement attacks in the SLIM dataset This example is shown for prediction of state anxiety in the SLIM dataset with resting-state connectomes and rCPM. In the top row, prediction with the original dataset shows poor performance (r ≈ 0). In the second row, as in Figure 2 , an enhancement pattern proportional to the state anxiety measure can be added to random edges to enhance performance while maintaining very high correlations between the original and enhanced connectomes (r ≈ 0.99). In the bottom row, an enhancement pattern can be added to specific subnetworks to alter interpretation. Here, we targeted the enhancement pattern to the salience subnetwork, and the resulting coefficients reflect that edges in the salience network dominate the prediction outcome.

Article Snippet: For all baseline regression models, we trained ridge-regression connectome-based predictive models (rCPM) in MATLAB (The MathWorks) with 10-fold cross-validation and a nested 10-fold cross-validation to select the L 2 regularization parameter, λ .

Techniques: