| Supervised learning: Classification | To assess how much this discrimination depends on the classification approach utilized rather than the underlying information content in the data, we employed three different representative classification techniques: penalized logistic regression (a regularized generalized linear model based on Lasso), regularized random forest (a tree-based model), and support vector machine (a kernel-based model). |
| Supervised learning: Classification | Fig 3 summarizes the classification results for ADCP by penalized logistic regression . |
| Supervised learning: Classification | Penalized logistic regression readily enables assessment of the relative importance of different features for classification. |
| Supervised learning: Regression | As with penalized logistic regression , the regularization employed by Lars in training seeks to force coefficients to zero and yield a sparse model. |
| readily interpretable. | Classification of ADCC from antibody features by penalized logistic regression . |
| readily interpretable. | Classification of cytokine release from antibody features by penalized logistic regression . |
| Relationship of Csparse+latent to orientation tuning and physical distances | Positive connectivity decreased with Aori (p < 0.005 in each of the five sites, t-test on the logistic regression coefficient) whereas negative connectivity did not decrease (Fig. |
| Relationship of Csparse+latent to orientation tuning and physical distances | 7 G): The slope in the logistic model of connectivity with respect to Aori was significantly higher for positive than for negative interactions (p < 0.04 in each of the five sites, two-sample t-test of the difference of the logistic regression coefficient). |
| Relationship of Csparse+latent to orientation tuning and physical distances | Positive connectivity decayed with distance (p < 10—6 in each of the five sites for positive interactions, t-test on the logistic regression coefficient in normalized data) (Fig. |
| Exclusion of participants | Our main analyses regarded participant’s choice behavior, but a detailed analysis of response times and a logistic regression of choices were also performed (see 81 Text; 81 Fig). |
| Experiment 1: Dissociating effort and delay discounting | A logistic regression of participant’s choices furthermore showed that costs and magnitudes both influenced choices (81B Fig. |
| Supporting Information | B, Mean (i SEM) parameter estimates from a logistic regression analysis of each participant’s choice pattern. |
| Supporting Information | B, To illustrate that the percentage of choices explained by a model depends on the offered choice stimuli (see Results), we repeated the model fitting and logistic regression analysis on the highlighted subset of trials which was defined as choices in which (a) both options were in the lower cost range, with at least one option with cost <02, and the difference to the second option not higher than 0.3, or (b) both options were in the higher range of cost levels (both costs>0.4). |