Automated Binning for Nonlinear Data Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data modeling systems face inefficiencies in binning non-linear data, often resulting in too few or too many bins, inconsistent bin ranges, and difficulties in managing data variability, as the manual selection of bins can lead to sub-optimal performance and loss of data.
Innovation Solution
An automated data modeling module determines the number and range of bins for non-linear data models using a training data set and validation data set, employing techniques like Gini coefficient and entropy to generate decision trees, and merges these trees to ensure monotonic weight of evidence across nodes, thereby optimizing binning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual bin selection is used, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system automatically determines optimal bin ranges and numbers by analyzing the data distribution and model requirements itself, without requiring manual intervention. The data modeling system performs self-service binning by computing statistical properties and generating bin configurations autonomously based on the training data characteristics.
Solution Approach 2:
The system dynamically adjusts binning parameters (number of bins, bin ranges, bin boundaries) based on the specific characteristics of the data and model requirements. Instead of using fixed manual bins, the system modifies bin parameters to optimize for the particular dataset and modeling task at hand.
2Device complexity
If too few bins are used, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The system determines the optimal number of bins by analyzing data distribution and model requirements, using just enough bins to preserve necessary information without excessive complexity. It applies partial binning where different variables may have different numbers of bins based on their specific characteristics and contribution to the model.
Solution Approach 2:
The system dynamically adjusts the number of bins as a variable parameter based on data characteristics and model needs, rather than using a fixed number. It computes optimal bin counts that balance information preservation with model complexity constraints.
3Manufacturing precision
If too many bins are used, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system uses just enough bins to achieve the necessary precision for the modeling task, avoiding excessive binning that would increase complexity without providing proportional benefits. It applies partial binning strategies where different variables receive different numbers of bins based on their specific needs.
Solution Approach 2:
The system makes the number of bins dynamic rather than static, adjusting bin counts based on data characteristics and model requirements. The binning configuration adapts to the specific dataset and modeling task, allowing optimal precision without fixed complexity.
4Manufacturing precision
If automated bin determination is used, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The data modeling system performs automated bin determination autonomously by analyzing data distribution, computing statistical properties, and generating optimal bin configurations without user intervention. The system serves itself by automatically configuring bins based on its analysis of the training data and model requirements.
Solution Approach 2:
The system replaces manual bin selection processes with automated computational methods, substituting human judgment with algorithmic analysis of data characteristics. It uses computational techniques to determine bin parameters that would be difficult or time-consuming to establish manually.
5Adaptability or versatility
If inconsistent bin ranges are used, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The system applies different binning strategies and range configurations to different variables based on their specific characteristics and requirements. Each variable receives locally optimized bin ranges tailored to its data distribution and importance to the model, rather than applying uniform binning across all variables.
Solution Approach 2:
The system dynamically adjusts bin range parameters for each variable based on its specific characteristics, allowing different scales, boundaries, and configurations as needed. It modifies bin parameters locally for each variable while maintaining overall consistency through automated determination.
Data Source
AI summary
The systems, methods, and computer program products for determining bins for a data model are provided. Variables in a training data set are binned into bins up to a configurable number of bins. Variables in the validation data set are also binned using the bins from the training data set. A first decision tree is generated using the bins and the binned variables from the training data set and is pruned. A second decision tree is generated using the structure of the first decision tree and the binned variables from the validation data set. The first and second decision tree are merged into a third decision tree. Leaf nodes of the third decision tree are sorted and merged until weights of evidence associated with the training data set and the validation data set are monotonic. The bins for the data model are determined from the merged leaf nodes.