Dynamic subscriber network physical impairment detection techniques

Systems and techniques are disclosed for using machine learning to dynamically detect physical impairments in lines of a subscriber network. In some implementations, per-tone data for a line of a subscriber network and data indicating a set of one or more scores is obtained. Each score included in the set of scores indicates a conditional likelihood that the line has a type of impairment with respect to a different feature subset ensemble. The per-tone data and the data indicating the set of one or more scores is provided as input to a model. The model is trained to output, for each of different sets of feature subset ensembles, a confidence score representing an overall likelihood that a particular line has a physical impairment. Data indicating a particular confidence score representing an overall likelihood that the line has the physical impairment is obtained. The particular confidence score is provided for output.