Decision Tree Node Pruning for Model Efficiency
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Solution Overview
Problem
Business organizations face barriers in utilizing machine learning due to the requirement of statistical and AI expertise, computational intensity, and challenges in handling large datasets for accurate predictions, particularly for infrequent events like financial fraud detection, where traditional mechanisms for data transformation are less than optimal.
Innovation Solution
A customizable machine learning service (MLS) with programmatic interfaces that guide non-expert users through machine learning best practices, allowing default settings derived from accumulated experience, and enabling expert customization, along with a recipe language for feature processing steps, and the use of a provider network with distributed computing and storage for handling large datasets while ensuring security and isolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning techniques are used, then prediction accuracy can be improved, but the requirement for statistical and AI expertise increases
Solution Approach 1:
The patent introduces an automated model selection and configuration system that acts as an intermediary between the user and complex machine learning algorithms. The system automatically selects appropriate algorithms, tunes hyperparameters, and configures models based on the problem type and data characteristics, eliminating the need for users to have statistical and AI expertise while maintaining high prediction accuracy
Solution Approach 2:
The system performs self-service by automatically selecting algorithms, configuring parameters, and optimizing models without human intervention. The automated machine learning pipeline independently handles data preprocessing, feature engineering, model selection, and hyperparameter tuning, allowing non-experts to achieve expert-level results
2Measurement precision
If machine learning models are trained on large datasets to improve prediction accuracy, then the computational resources required increase
Solution Approach 1:
The system applies partial action by automatically selecting and training only the necessary subset of models required for the specific problem, rather than exhaustively training all possible algorithms. The automated selection process identifies the most appropriate models based on data characteristics and problem requirements, reducing unnecessary computational waste while maintaining prediction accuracy
Solution Approach 2:
The system dynamically adjusts computational parameters such as dataset size, model complexity, and training iterations based on the problem requirements and available resources. The automated configuration process optimizes these parameters to achieve the best balance between prediction accuracy and computational efficiency for each specific use case
3Measurement precision
If transformations are applied to input data variables to improve model performance, then the complexity of data preprocessing increases
Solution Approach 1:
The system performs self-service in data preprocessing by automatically selecting and applying appropriate transformations based on the characteristics of input variables and the target problem. The automated pipeline independently determines which transformations (e.g., normalization, encoding, feature engineering) are necessary without requiring manual configuration, thereby improving model performance while keeping the user interface simple
Data Source
AI summary
During a training phase of a machine learning model, representations of at least some nodes of a decision tree are generated and stored on persistent storage in depth-first order. A respective predictive utility metric (PUM) value is determined for one or more nodes, indicating expected contributions of the nodes to a prediction of the model. A particular node is selected for removal from the tree based at least partly on its PUM value. A modified version of the tree, with the particular node removed, is stored for obtaining a prediction.