MACHINE LEARNING MODEL SELECTION AND EXPLANATION FOR MULTI-DIMENSIONAL DATASETS
In general, techniques are described for various aspects of accessing datasets. A device comprising a memory configured to store the multi-dimensional dataset; a processor may perform the techniques. The processor may apply a plurality of machine learning models to the multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models. The processor may next determine a correlation of one or more dimensions of the multi-dimensional dataset to the results output by each of the machine learning models, and select, based on the correlation determined between the dimensions and the result output by each of the machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the machine learning models. The processor may then output the result for each of the subset of the plurality of machine learning models.
1 . A device configured to interpret a multi-dimensional dataset, the device comprising:
a memory configured to store the multi-dimensional dataset; and
one or more processors configured to:
apply a plurality of machine learning models to the multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
determine a correlation of one or more dimensions of the multi-dimensional dataset to the results output by each of the plurality of machine learning models;
select, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models; and
output the result for each of the subset of the plurality of machine learning models.
2 . The device of claim 1 , wherein the one or more processors are configured to output the result as a sentence using plain language.
3 . The device of claim 1 , wherein the one or more processors are configured to output the result for at least one of the subset of the plurality of machine learning models as a graph identifying a relevance of each of the one or more dimensions to the result for each of the subset of the plurality of machine learning models.
4 . The device of claim 3 , wherein the graph comprises an impact graph.
5 . The device of claim 1 , wherein the one or more processors are configured to output the result for each of the subset of the plurality of machine learning models as a graphical representation of a decision tree.
6 . The device of claim 1 , wherein the one or more processors are further configured to:
determine, based on a comparison of the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models to a relevance threshold, one or more low relevance dimensions of the multi-dimensional dataset that have low relevance to the result output by each of the plurality of machine learning models; and
output an indication explaining that the one or more low relevance dimensions have low relevance to the result.
7 . The device of claim 6 , wherein the one or more processors are configured to output a sentence in plain language that explain the one or more low relevance dimensions having low relevance to the result.
8 . The device of claim 1 , wherein the one or more processors are further configured to refrain from transforming the one or more dimensions of the multi-dimensional dataset prior to application of the plurality of machine learning models.
9 . The device of claim 1 , wherein the one or more processors are further configured to:
determine, based on the results for each of the one or more of the plurality of machine learning models, one or more of a plurality of charts to explain the corresponding result;
rank the one or more of the plurality of charts to identify a highest ranked chart;
select the highest ranked chart; and
output the highest ranked chart as a visual chart.
10 . The device of claim 9 , wherein the one or more processors are further configured to:
generate an explanation in plain language explaining a formulation of the visual chart; and
output the explanation.
11 . The device of claim 1 , wherein the one or more processors are further configured to:
generate a pipeline report explaining how the device produced the plurality of the machine learning models; and
output the pipeline report.
12 . A method of interpreting a multi-dimensional dataset, the method comprising:
applying a plurality of machine learning models to the multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
determining a correlation of the one or more dimensions of the multi-dimensional dataset to the results output by each of the plurality of machine learning models;
selecting, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models; and
outputting the result for each of the subset of the plurality of machine learning models.
13 . The method of claim 12 , wherein outputting the result comprises outputting the result as a sentence using plain language.
14 . The method of claim 12 , wherein outputting the result comprises outputting the result for at least one of the subset of the plurality of machine learning models as a graph identifying a relevance of each of the one or more dimensions to the result for each of the subset of the plurality of machine learning models.
15 . The method of claim 14 , wherein the graph comprises an impact graph.
16 . The method of claim 12 , wherein outputting the result comprises outputting the result for each of the subset of the plurality of machine learning models as a graphical representation of a decision tree.
17 . The method of claim 12 , further comprising:
determining, based on a comparison of the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models to a relevance threshold, one or more low relevance dimensions of the multi-dimensional dataset that have low relevance to the result output by each of the plurality of machine learning models; and
outputting an indication explaining that the one or more low relevance dimensions have low relevance to the result.
18 . The method of claim 17 , wherein outputting the indication comprises outputting a sentence in plain language that explain the one or more low relevance dimensions having low relevance to the result.
19 . The method of claim 12 , further comprising refraining from transforming the one or more dimensions of the multi-dimensional dataset prior to application of the plurality of machine learning models.
20 . The method of claim 12 , further comprising:
determining, based on the results for each of the one or more of the plurality of machine learning models, one or more of a plurality of charts to explain the corresponding result;
ranking the one or more of the plurality of charts to identify a highest ranked chart;
selecting the highest ranked chart; and
outputting the highest ranked chart as a visual chart.
21 . The method of claim 20 , further comprising:
generating an explanation in plain language explaining a formulation of the visual chart; and
outputting the explanation.
22 . The method of claim 12 , further comprising:
generating a pipeline report explaining how the device produced the plurality of the machine learning models; and
outputting the pipeline report.
23 . A non-transitory computer-readable storage medium storing instructions that, when executed, cause one or more processors to:
apply a plurality of machine learning models to a multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
determine a correlation of the one or more dimensions of the multi-dimensional dataset to the result output by each of the plurality of machine learning models;
select, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models; and
output the result for each of the subset of the plurality of machine learning models.