Predictive Model Generation via N-Dimensional Vector Projection
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Solution Overview
Problem
Current machine learning technologies face challenges in effectively generating predictive models from historical data elements to accurately predict trends and metrics in new data samples, particularly in web environments, where features may not be represented by existing axes in the n-dimensional space.
Innovation Solution
The method involves extracting key-value pairs from historical data elements, projecting them onto an n-dimensional space, and using a model generator, such as a support vector machine (SVM) or neural network, to generate predictive models. When features are not represented by axes, an orthogonality relationship is used to project values onto new axes, and the model is validated and retrained based on error thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used to generate predictive models from historical data, then the model generation process can be completed, but the accuracy of predicting trends and metrics in new data samples is insufficient
Solution Approach 1:
The patent transforms historical data elements into n-dimensional vectors by extracting key-value pairs and projecting them onto an n-dimensional space. Each dimension represents a specific feature, allowing the model to capture complex relationships and patterns in the data that traditional methods miss, thereby improving prediction accuracy for web metrics.
Solution Approach 2:
The system dynamically adjusts model parameters including the number of dimensions n, feature extraction parameters, and projection parameters based on the characteristics of the historical data. This adaptive parameter adjustment optimizes the predictive model for different types of web data and prediction tasks, enhancing both accuracy and reliability.
2Adaptability or versatility
If features are projected onto existing axes in n-dimensional space, then the model structure remains simple, but features not represented by existing axes cannot be effectively captured
Solution Approach 1:
When a feature is not represented by an existing axis, the system creates a new axis in the n-dimensional space and projects the feature value onto it. This dynamic dimension expansion allows the model to accommodate any feature from the historical data without being constrained by a fixed feature set, significantly improving adaptability while maintaining a systematic approach to model structure.
3Measurement precision
If the predictive model is continuously retrained with new historical data, then the model remains accurate, but the computational time and resources increase
Solution Approach 1:
The system implements a feedback mechanism where the predictive model is validated against new historical data, and retraining is triggered only when validation errors exceed a predefined threshold. This conditional retraining approach maintains model accuracy by updating only when necessary, avoiding unnecessary computational waste and reducing overall retraining time while ensuring the model remains effective.
Data Source
AI summary
A method of machine learning for generating a predictive model of a response characteristic based on historical data elements using a processor may include receiving historical data elements and historical values for the response characteristic related to uses of the historical data elements in web pages. A plurality of key-value pairs may be generated defining values of a plurality of predefined features representing properties of the historical data elements. Each of a plurality of n features may be represented by an axis in an n-dimensional space are extracted from the historical data elements. The extracted plurality of key-value pairs for each historical data element may be projected onto the n-dimensional space. The plurality of vectors may be input into a model generator to generate a predictive model predicting a value of the response characteristic for a new data element.