1. A method comprising: accessing a user-editable configuration file accessible to an operator of a response prediction system, the user-editable configuration file including a user specification of data accessible via external data sources, user-specified data encoding rules specifying specific portions of the data to be encoded into specific portions of one or more feature vectors, and user-specified assembly rules, the data including member data associated with a particular member and content data associated with a particular content item; encoding, using a plurality of source modules, the data accessible via the external data sources into the feature vectors, based on the user-specified data encoding rules; assembling, using an assembler module, one or more of the feature vectors into an assembled feature vector, based on the user-specified assembly rules included in the user-editable configuration file; determining that a particular one of the feature vectors is not available from a particular one of the source modules; in response to determining that the particular one of the feature vectors is not available, ignoring the particular feature vector based on a user-specified importance value associated with the particular feature vector; inserting a user-specified substitute value associated with the particular feature vector into a portion of the assembled feature vector associated with the particular feature vector; and performing, using a prediction module, a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item.
1. A method comprising: accessing a user-editable configuration file accessible to an operator of a response prediction system, the user-editable configuration file including a user specification of data accessible via external data sources, user-specified data encoding rules specifying specific portions of the data to be encoded into specific portions of one or more feature vectors, and user-specified assembly rules, the data including member data associated with a particular member and content data associated with a particular content item; encoding, using a plurality of source modules, the data accessible via the external data sources into the feature vectors, based on the user-specified data encoding rules; assembling, using an assembler module, one or more of the feature vectors into an assembled feature vector, based on the user-specified assembly rules included in the user-editable configuration file; determining that a particular one of the feature vectors is not available from a particular one of the source modules; in response to determining that the particular one of the feature vectors is not available, ignoring the particular feature vector based on a user-specified importance value associated with the particular feature vector; inserting a user-specified substitute value associated with the particular feature vector into a portion of the assembled feature vector associated with the particular feature vector; and performing, using a prediction module, a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item. 13. The method of claim 1 , wherein the content item is an advertisement, and wherein the user action is any one of a click response, a non-click response, a hover response, and a conversion response.