Categorical Input Machine Learning Models for Interpretable Predictions
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Solution Overview
Problem
Existing predictive data analysis systems face challenges in handling categorical input data due to issues like noise introduction when imposing ordinality, overfitting, and the lack of interpretability, which affects accuracy and reliability, especially in high-impact business decisions where explanations are required.
Innovation Solution
The development of methods that utilize categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering, and training categorical input machine learning models to generate interpretable predictions by refining features and removing noise, ensuring that only predictive features are used, thereby improving model accuracy and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If categorical data is processed using existing predictive data analysis systems, then predictions can be generated, but noise is introduced and overfitting occurs which reduces accuracy and reliability
Solution Approach 1:
The patent extracts and removes harmful noise from categorical data through a filtering process. The system identifies and eliminates noisy categories by comparing training data distributions with inference data distributions, extracting only the meaningful predictive signals while discarding noise that would otherwise degrade model reliability
Solution Approach 2:
The patent performs preliminary noise filtering and category alignment before the actual prediction process. By pre-processing the categorical data to remove noisy categories and align training/inference data distributions, the system prevents noise from affecting the prediction model in the first place, rather than attempting to correct it afterward
2Reliability
If categorical data is processed using existing predictive data analysis systems, then predictions can be generated, but overfitting occurs which reduces model generalization
Solution Approach 1:
The patent extracts only the essential predictive information from categorical data by filtering out noisy categories. This selective extraction prevents the model from learning spurious patterns that would lead to overfitting, while retaining the meaningful signals needed for generalization
Solution Approach 2:
The patent changes the parameters of categorical features by aligning the distribution of categories between training and inference data. This parameter adjustment ensures that the model generalizes well to new data by preventing overfitting to specific training data category distributions
3Ease of operation
If categorical data is processed using existing predictive data analysis systems, then predictions can be generated, but interpretability is lacking which affects decision-making
Solution Approach 1:
The patent introduces an intermediary explanation layer that translates model predictions into human-interpretable insights. This intermediary component provides explanations for predictions by showing which categorical features and categories were most influential, making the decision-making process transparent without affecting model accuracy
Data Source
AI summary
There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.