Random forests are a method for predicting numerous ensemble learning tasks. Prediction variability can illustrate how influential the training set is for producing the observed random forest predictions and provides additional information about prediction accuracy. forest-confidence-interval is a Python module for calculating variance and adding confidence intervals to scikit-learn random forest regression or classification objects. The core functions calculate an in-bag and error bars for random forest objects. Our software is designed for individuals using scikit-learn random forest objects that want to add estimates of uncertainty to random forest predictors.