ML Feature Binning for Predictive Accuracy
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Solution Overview
Problem
Machine learning models face challenges in identifying predictive features and values, particularly due to large value ranges and limited control over user attributes, leading to low precision and recall in predicting user actions.
Innovation Solution
A system and method using machine learning techniques to train models based on usage data, generating dependency graphs and histograms to determine predictive features and values, focusing on attributes that producers can control and influence, while leveraging Gradient Boosting for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attributes with large value ranges are divided into multiple fine-grained features, then predictive accuracy improves, but model complexity and manual effort increase significantly
Solution Approach 1:
The patent segments the continuous attribute space into discrete bins using decision trees, automatically creating fine-grained features without manual intervention. The decision tree algorithm partitions the feature space based on data patterns, transforming continuous values into categorical bins that capture predictive relationships while maintaining manageable model complexity through automated structure learning.
Solution Approach 2:
The system performs self-service by automatically generating features and determining optimal binning strategies without human intervention. The machine learning model analyzes the data distribution and autonomously creates the feature hierarchy, eliminating the need for manual feature engineering while maintaining high predictive accuracy through data-driven decisions.
2Measurement precision
If manual feature engineering is performed for each possible range, then predictive precision improves, but time consumption increases significantly
Solution Approach 1:
The patent implements self-service through automated machine learning algorithms that independently perform feature generation, binning strategy optimization, and model training. The system processes large datasets and generates predictive features automatically, reducing time consumption from manual engineering to automated computation while maintaining high predictive precision through algorithmic optimization.
Solution Approach 2:
The system changes the approach from manual parameter specification to automated parameter learning. Instead of manually defining bin boundaries and feature transformations, the machine learning algorithm automatically determines optimal parameter values based on data patterns, significantly reducing time consumption while improving predictive precision through data-driven parameter selection.
3Adaptability or versatility
If training data is used for attributes with large value ranges, then model completeness improves, but data sparsity causes inaccurate or noisy weights for fine-grained features
Solution Approach 1:
The patent segments the continuous attribute space into discrete bins that balance coverage and data density. By partitioning the feature space strategically, the system ensures that each bin contains sufficient data for reliable weight estimation while collectively covering the full range of possible values. This segmentation approach maintains model completeness without creating sparsely populated fine-grained features that would produce noisy weights.
Data Source
AI summary
Techniques are provided for using machine learning techniques to identify predictive features and predictive values for each feature. In one technique, a model is trained based on training data that comprises training instances, each of which corresponds to multiple usage-based features of an online service by a user. For each usage-based feature in a subset of the usage-based features, the model is used to generate a dependency graph, a histogram is generated, and an optimized value is selected based on the dependency graph and the histogram. A user of the online service is identified, along with a usage value that indicates a level of usage, by the user, of a usage-based feature. A comparison between the usage value and an optimized value of the usage-based feature is performed. Based on the comparison, it is determined whether to present data about that usage-based feature to the user.