1. A system for facilitating interactive feature selection for machine learning, the method comprising: one or more memory devices; and one or more processors configured to: provide a first training set of data items, wherein one or more of the data items have been previously labeled with example labels of a particular class of data item; provide a classifier to be trained to determine labels for data items; utilize the classifier to determine predicted labels for one or more of the data items that have been previously labeled with example labels; identify one or more data items from the first training set having a discrepancy between a respective example label and a predicted label that was determined by the classifier; present via a user interface an indication of the one or more data items from the first training set having the discrepancy between the respective example label and the predicted label, wherein the user interface includes a feature-selection interface configured to receive a user selection of one or more features that are utilized as input features to train the classifier and improve the accuracy of the predicted labels; receive, via the user interface, the user selection of one or more features; and train the classifier with the one or more user-selected features as input features; wherein the one or more processors are further configured to iteratively repeat the steps of utilize, identify, present, receive, and train, with the first training set until a user input is received which indicates that feature selection is complete.