Mood classification from passive data promises to provide an unobtrusive way to track a person's emotions over time. In this exploratory study, we collected phone sensor data and physiological signals from 8 individuals, including 5 healthy participants and 3 depressed patients, for a maximum of 35 days. Participants were asked to answer a digital questionnaire three times daily, resulting in a total of 334 self-reported mood state samples. Gradient-boosting classification was applied to the collected passive data to categorize 4 mood states in the Valence-Energetic Arousal space. The cross-validation results showed better classification performance compared to a baseline model, which always predicts the majority class. The classifier using passive data had an area under the precision-recall curve of 0.39 (SD = 0.1) while the baseline had 0.26 (SD = 0.03), suggesting the presence of information in the collected features that support the classification process. The model identified the entropy of the heart rate and the average physical activity in the preceding 8 hours, along with the max normal-to-normal (NN) sinus beat interval and the NN low frequency-high frequency ratio during the questionnaire completion, as the most important features in its analysis. Additionally, the time range of data collection was considered a contextual factor.