The ML task is to predict the battery lifetime, and to compare the impact of incorporating features encoding cycling conditions directly or indirectly ("Class 0" and "Class 1"). We employed a regularized linear model (elastic net) and an ensemble model (random forest regressor), the latter being able to capture nonlinear correlations. Most of the discussion is based on the results from the random forest model, which provides better accuracy. To prevent overfitting and to optimize model hyperparameters, a 10-fold cross-validation was carried out systematically using the GridSearchCV class from Python package SKlearn (see SI for details). An 80-20 train-test split was used. Because we observe that the prediction accuracy depends on train-test splits, all of the analyses conducted here are repeated over 10 random train-test splits. To evaluate the usefulness of an ML approach, we employed a "dummy regressor" from the SKlearn library as a baseline, which always predicts the same output value (mean of the training set) regardless of the inputs.