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 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.
1 . A machine learning structure generator accelerator apparatus, comprising:
a memory;
a component collection in the memory, including:
a dynamic feature determining 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 dynamic feature determining component, stored in the memory, to:
obtain, via at least one processor, a dataset comprising a set of features;
partition, via at least one processor, contents of the dataset into a features dataframe and a labels dataframe;
encode, via at least one processor, features data in the features dataframe;
calculate, via at least one processor, a score for each feature in the features dataframe;
determine, via at least one processor, top features in the features dataframe based on the calculated scores; and
provide, via at least one processor, the determined top features to a machine learning structure generator.
2 . The apparatus of claim 1 , wherein the dataset comprises log level data, and wherein each row of the log level data represents a purchased ad.
3 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the dynamic feature determining component, stored in the memory, to:
filter, via at least one processor, the dataset such that data regarding impressions that resulted in a conversion is kept, and a specified fraction of data regarding impressions that did not result in a conversion is kept.
4 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the dynamic feature determining component, stored in the memory, to:
enrich, via at least one processor, the dataset by converting a feature into a plurality of features.
5 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the dynamic feature determining component, stored in the memory, to:
determine, via at least one processor, a set of features in the dataset to combine into a combined feature; and
add, via at least one processor, the combined feature to the dataset.
6 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the dynamic feature determining component, stored in the memory, to:
drop, via at least one processor, an unusable feature from the dataset.
7 . The apparatus of claim 6 , wherein an unusable feature is a feature that is not available during bid time.
8 . The apparatus of claim 6 , wherein an unusable feature is a feature with fewer than a specified number of values.
9 . The apparatus of claim 6 , wherein an unusable feature is a feature included in a set of features to exclude.
10 . The apparatus of claim 1 , wherein the features data is encoded by label encoding string data.
11 . The apparatus of claim 1 , wherein the features data is encoded by one-hot-encoding categorical features.
12 . The apparatus of claim 1 , wherein scores are calculated using a Chi Square Test.
13 . The apparatus of claim 1 , wherein scores are calculated using a Random Forest method.
14 . The apparatus of claim 1 , wherein the labels dataframe comprises a labels column that specifies for each row of the features dataframe whether a row is associated with a conversion, and wherein a score for each feature in the features dataframe is calculated based on the dependence of a feature on the labels column.
15 . The apparatus of claim 1 , further, comprising:
the processor issues instructions from the dynamic feature determining component, stored in the memory, to:
prune, via at least one processor, the scored features in the features dataframe such that the highest scored feature from a group of same type features remains for consideration as a top feature.