A primary goal for Chesapeake Bay watershed restoration is to improve stream health and function in 10% of stream miles by 2025. Predictive spatial modeling of stream conditions, when accurate, is one method to fill gaps in monitoring coverage and estimate baseline conditions for restoration goals. Predictive modeling can also monitor progress as additional data become available. We developed a random forests model to predict biological condition of small streams (<200 km² in drainage) in the Chesapeake Bay watershed. Biological condition was measured with the Chesapeake Bay Basin-wide Index of Biotic Integrity (Chessie BIBI), a stream macroinvertebrate index. Our goal was to predict biological condition in all unsurveyed small streams present in a 1:24,000 scale catchment layer as a 2004–2008 baseline. We reclassified the 5-category Chessie BIBI ratings into two categories, poor and fair/good, to align with management goals of the Chesapeake Bay Program. The model included 12 geospatial predictor variables including measures on spatial location, bioregion, land cover, soil, precipitation, and number of dams in local catchments. We trained the model with a random 75% subset of Chessie BIBI data (n = 1449), and used the remaining 25% of Chessie BIBI data (n = 484) as test data. The model performed well, correctly predicting 72% of samples in training data and 73% of samples in test data, but model accuracy varied among bioregions. We performed uncertainty analyses by adding bands of either ±0.05 or ±0.10 BIBI units to the cutoff between poor and fair/good. These uncertainty analyses resulted in 14.5% (±0.05 band) and 24.8% (±0.10 band) of samples in test data being classified as in uncertain condition. For 95,877 small stream reaches in the Chesapeake Bay watershed, the model predicted 64% in fair/good condition, the ±0.05 uncertainty analyses predicted 57% in fair/good condition, and the ±0.10 uncertainty analysis predicted 50% in fair/good condition. These reported values have different implications for the number of improved stream miles required to meet the goal of improving 10%. Incorporating uncertainty provides an assessment of model strength as well as confidence in predictions. We, therefore, suggest increased reporting of uncertainty in studies that spatially predict stream conditions.