Constrained Decision Tree Ensemble for Rainfall Probability
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Solution Overview
Problem
Existing decision tree ensemble models face challenges in accurately predicting outcomes due to high complexity in variable relationships, leading to uncertainty and a lack of trust in model explanations, as they often generate multiple inferences about each variable's effect on the target variable.
Innovation Solution
A method is introduced that applies a directionality constraint during decision tree ensemble learning by calculating a measure of directionality for each partition, performing a constrained node selection process to maintain consistent directionality, and updating a directionality table to ensure consistent inference patterns, thereby generating a more explainable model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If decision tree ensemble learning is applied to capture complex variable relationships, then prediction accuracy is improved, but model explainability deteriorates due to multiple inferences about each variable's effect
Solution Approach 1:
The patent introduces a directionality constraint parameter that restricts the learning process to select only those splits that maintain consistent directionality for each variable. This parameter change filters out conflicting inferences while preserving the ensemble's predictive power, thereby maintaining accuracy while improving explainability.
Solution Approach 2:
The directionality table acts as an intermediary mechanism that mediates between the complex variable relationships captured by the ensemble and the need for clear explanations. It records and enforces consistent directional effects for each variable, serving as a bridge between model complexity and interpretability.
2Adaptability or versatility
If unconstrained node selection is used to maximize model flexibility, then adaptability is improved, but model reliability deteriorates due to inconsistent directionality and uncertainty in variable effects
Solution Approach 1:
By introducing the directionality constraint parameter during node selection, the patent modifies the learning process to prioritize consistent directional effects. This parameter change ensures that while the model remains flexible in capturing variable relationships, it produces reliable and trustworthy predictions through consistent inference patterns.
Solution Approach 2:
The directionality table provides feedback during the learning process by tracking the directional effect of each variable. This feedback mechanism guides node selection to maintain consistency, thereby improving model reliability without sacrificing adaptability.
3Adaptability or versatility
If multiple inferences are generated for each variable's effect on the target, then comprehensive coverage of variable relationships is improved, but device complexity increases leading to difficulty in model interpretation
Solution Approach 1:
The patent extracts and enforces only the consistent directional inferences for each variable through the directionality constraint, while discarding conflicting or redundant inferences. This extraction process reduces interpretation complexity while maintaining comprehensive coverage of meaningful variable relationships.
Solution Approach 2:
The directionality constraint parameter transforms the learning process from generating multiple inferences to generating consistent, singular inferences for each variable. This parameter change simplifies model interpretation while preserving the essential variable relationships needed for accurate prediction.
Data Source
AI summary
A method for generating an artificial intelligence model for determining probability of rainfall, by applying a decision tree ensemble learning process on a dataset, the method comprising: receiving a first dataset comprising at least two variables; determining at least one split criteria for each variable within the first dataset; partitioning the first dataset based on each determined split criteria; calculating a measure of directionality for each partition of data; performing a constrained node selection process by selecting a candidate variable and split criteria, wherein the selection is made to keep a consistent directionality for the selected variable based on existing nodes; updating a directionality table at the end of a constrained node selection; reiterating the constrained node selection process for every node selection throughout the decision tree ensemble learning process until an ensemble model is generated; and processing a second dataset with the generated ensemble model to determine probability of rainfall; wherein the first dataset contains data received from one or more sensors, the received data including data pertaining to temperature.