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Description: Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and it is unlikely that all symptoms are associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identify the most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory – II. Performance of models was evaluated using predictive R-squared (𝑅2 𝑝𝑟𝑒𝑑), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the Self- Referent Encoding Task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression. General Scientific Summary: This study finds that many symptoms of depression are not strongly associated with thinking negatively about oneself or attending to negative information. This implies that negative cognitive biases may not be strongly associated with depression per se, but may instead contribute to the maintenance of specific depression symptoms, such as sadness, self-dislike, pessimism, feelings of punishment, and indecision.