Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems
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. The machine learning results are translated into commands and provided to the third party.
1 . A double blind machine learning apparatus, comprising:
a memory;
a component collection in the memory, including:
a double blind machine learning component;
a processor disposed in communication with the memory, and configured to issue a plurality of processing instructions from the component collection stored in the memory,
wherein the processor issues instructions from the double blind machine learning component, stored in the memory, to:
obtain, via at least one processor, a double blind machine learning request, wherein the double blind machine learning request includes: a minimum bid, a maximum bid, a look back window;
determine, via at least one processor, a third party's shared dataset for the look back window and external predictions data corresponding to the shared dataset, wherein the external predictions data is determined by the third party based on an unavailable dataset;
determine, via at least one processor, proprietary data corresponding to the shared dataset;
generate, via at least one processor, a dataframe comprising at least a subset of the determined shared dataset, at least a subset of the external predictions data, and at least a subset of the proprietary data;
determine, via at least one processor, a set of top features from the dataframe, wherein top features are features that are most likely to be useful for classification;
encode, via at least one processor, top features data associated with the determined set of top features;
generate, via at least one processor, a machine learning structure using the encoded top features data;
utilize, via at least one processor, the generated machine learning structure on the encoded top features data to produce machine learning results, wherein the machine learning results specify an efficacy value for a given set of top features values;
translate, via at least one processor, the produced machine learning results into commands, wherein the translated commands define a bid value for a given set of top features values based on the corresponding efficacy value, the minimum bid, and the maximum bid; and
provide, via at least one processor, the translated commands to the third party.
2 . The apparatus of claim 1 , wherein the shared dataset comprises log level data, and wherein each row of the log level data represents a purchased impression.
3 . The apparatus of claim 2 , wherein the external predictions data specifies an efficacy value calculated by the third party for each row of the log level data.
4 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the double blind machine learning component, stored in the memory, to:
filter, via at least one processor, the shared dataset such that data regarding impressions that resulted in a click is kept, and a specified fraction of data regarding impressions that did not result in a click is kept.
5 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the double blind machine learning component, stored in the memory, to:
determine, via at least one processor, a set of features in the generated dataframe to combine into a combined feature; and
add, via at least one processor, the combined feature to the dataframe.
6 . The apparatus of claim 1 , wherein instructions to determine a set of top features from the dataframe further comprise instructions to:
partition, via at least one processor, contents of the dataframe into a features dataframe and a labels dataframe;
determine, via at least one processor, a score for each feature in the features dataframe based on the dependence of a feature on the contents of the labels dataframe; and
determine, via at least one processor, top features in the features dataframe based on the determined scores.
7 . The apparatus of claim 6 , wherein instructions to determine a set of top features from the dataframe further comprise instructions to:
prune, via at least one processor, the scored features in the features dataframe to remove correlated features with smaller scores.
8 . The apparatus of claim 6 , wherein scores are determined using a Chi Square Test.
9 . The apparatus of claim 1 , wherein the top features data is encoded by label encoding string data.
10 . The apparatus of claim 1 , wherein the top features data is encoded by one-hot-encoding categorical features.
11 . The apparatus of claim 1 , wherein the machine learning structure is generated by optimizing parameters of logistic regression using a grid search.
12 . The apparatus of claim 11 , wherein the optimized parameters comprise: penalty for regularization, inverse of regularization strength.
13 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the double blind machine learning component, stored in the memory, to:
determine, via at least one processor, that the set of top features includes a proprietary feature from the proprietary data; and
provide, via at least one processor, encoded proprietary data corresponding to the proprietary feature to the third party.
14 . The apparatus of claim 1 , wherein the translated commands are in a Bonsai tree format.
15 . The apparatus of claim 1 , wherein the translated commands are executable commands in JSON format.