Abstract: The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A decoupled machine learning workflow generation request is obtained. A set of decoupled tasks specified via the decoupled machine learning workflow generation request is determined, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class. Dependencies among decoupled tasks in the set of decoupled tasks are determined. A decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies is generated, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.

Abstract: The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A double blind machine learning request is obtained. A third party's shared dataset and corresponding external predictions data determined by the third party based on an unavailable dataset is determined. Proprietary data corresponding to the shared dataset is determined. A dataframe comprising at least subsets of the determined shared dataset, external predictions data, and proprietary data is generated. A set of top features from the dataframe is determined. Top features data is utilized to generate a machine learning structure. The generated machine learning structure is utilized to produce machine learning results.

Abstract: The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A user interface configuration request associated with a dataset comprising a set of features is obtained. A set of top features associated with the dataset that are most likely to be useful for machine learning classification is determined. A feature user interface configuration associated with each top feature in the set of top features is added to an overall machine learning guided user interface configuration. The overall machine learning guided user interface configuration is provided for a user.

Abstract: The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A decoupled machine learning workflow generation request is obtained. A set of decoupled tasks specified via the decoupled machine learning workflow generation request is determined, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class. Dependencies among decoupled tasks in the set of decoupled tasks are determined. A decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies is generated, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.

Abstract: The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A dataset comprising a set of features is obtained. Contents of the dataset are partitioned into a features dataframe and a labels dataframe. Features data in the features dataframe is encoded. A score for each feature in the features dataframe is calculated. Top features in the features dataframe are determined based on the calculated scores. The determined top features are provided to a machine learning structure generator.