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 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.
1 . A machine learning guided user interface configuring apparatus, comprising:
a memory;
a component collection in the memory, including:
a user interface configuring 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 user interface configuring component, stored in the memory, to:
obtain, via at least one processor, a user interface configuration request, wherein the user interface configuration request is associated with a dataset comprising a set of features;
determine, via at least one processor, a set of top features associated with the dataset, wherein top features are features that are most likely to be useful for machine learning classification;
add, via at least one processor, a feature user interface configuration associated with each top feature in the set of top features to an overall machine learning guided user interface configuration; and
provide, via at least one processor, the overall machine learning guided user interface configuration for a user.
2 . The apparatus of claim 1 , wherein the dataset comprises log level data associated with an advertising campaign.
3 . The apparatus of claim 2 , wherein the set of top features associated with the dataset is retrieved from a repository associated with the advertising campaign.
4 . The apparatus of claim 1 , wherein the set of top features associated with the dataset is specified via a tool configuration setting associated with a tool, wherein the tool configuration setting is determined based on a prior analysis of features data associated with the dataset.
5 . The apparatus of claim 1 , wherein instructions to determine a set of top features associated with the dataset further comprise instructions to:
partition, via at least one processor, contents of the dataset 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.
6 . The apparatus of claim 1 , wherein a feature user interface configuration is pre-built for each feature that is selectable as a top feature.
7 . The apparatus of claim 1 , wherein a feature user interface configuration facilitates configuring how to set bids for a campaign depending on the associated top feature's value.
8 . The apparatus of claim 1 , wherein the overall machine learning guided user interface configuration facilitates configuring how to set bids for a campaign based on a multidimensional space comprising dimensions corresponding to the set of top features.
9 . The apparatus of claim 1 , wherein the overall machine learning guided user interface configuration is configured to include a bid curve optimized based on top features data associated with the determined set of top features.
10 . The apparatus of claim 1 , further, comprising:
a campaign optimization component in the component collection;
wherein the processor issues instructions from the campaign optimization component, stored in the memory, to:
obtain, via at least one processor, optimization input from the user;
determine, via at least one processor, campaign optimization input parameters specified via the optimization input;
optimize, via at least one processor, a bid curve for a campaign associated with the dataset based on the campaign optimization input parameters and top features data associated with the set of top features; and
provide, via at least one processor, the overall machine learning guided user interface configuration that includes the optimized bid curve for the user.
11 . The apparatus of claim 10 , wherein the campaign optimization input parameters include changes to the set of top features utilized for optimization.
12 . The apparatus of claim 10 , wherein the campaign optimization input parameters include changes to data points of the bid curve.
13 . The apparatus of claim 10 , further, comprising:
the processor issues instructions from the campaign optimization component, stored in the memory, to:
translate, via at least one processor, the optimized bid curve into commands, wherein the translated commands define a bid value for a given set of top features values; and
provide, via at least one processor, the translated commands to a third party.
14 . The apparatus of claim 13 , wherein the translated commands are in a Bonsai tree format.
15 . The apparatus of claim 13 , wherein the translated commands are executable commands in JSON format.