Precomputed Vectors and Feature Selection for ML Likelihood Scores
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Solution Overview
Problem
Existing methods for predicting conditions using machine learning models are inefficient and lack accuracy due to the selection of irrelevant features and the use of unsuitable algorithms, leading to suboptimal performance in generating likelihood scores.
Innovation Solution
A machine learning model is developed using pre-computed vectors derived from a training dataset, with feature selection based on statistical significance and random forest variable importance, and employing algorithms like random forests and support vector machines to generate processed data for improved likelihood scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used with all available features, then the model can process comprehensive data, but the accuracy decreases due to irrelevant features and suboptimal algorithm selection
Solution Approach 1:
The patent extracts and removes irrelevant features from the dataset using statistical significance testing (p-value thresholding) and Random Forest variable importance metrics. This extraction process eliminates features that do not contribute to prediction accuracy, resolving the contradiction by keeping only the most relevant features for the machine learning model.
Solution Approach 2:
The patent performs preliminary feature selection and algorithm optimization before the main prediction process. By pre-computing feature importance metrics and selecting optimal algorithms in advance, the system establishes a refined feature set and optimal model configuration that improves prediction accuracy without adding complexity during actual prediction.
2Loss of information
If more features are included in the machine learning model, then more information is processed, but the model performance deteriorates due to irrelevant features
Solution Approach 1:
The patent changes the parameter of feature relevance by applying statistical significance thresholds (p-value < 0.05) and Random Forest importance criteria. This parameter transformation converts a large set of features into a optimized subset that retains essential information while eliminating noise, thus improving model reliability without significant information loss.
3Productivity
If conventional algorithms are used without optimization, then the implementation is simple, but the likelihood score generation is inefficient and inaccurate
Solution Approach 1:
The patent introduces dynamic algorithm selection and optimization processes that adapt to the specific characteristics of the dataset and prediction task. By implementing multiple algorithms with optimized parameters and selecting the best performing ones based on validation results, the system achieves efficient and accurate likelihood score generation while managing algorithmic complexity through systematic evaluation.
Data Source
AI summary
In some embodiments, a machine learning model may be accessed and used to generate a likelihood score related to a condition. In some embodiments, pre-computed vectors may be derived from a training dataset used to build the machine learning model, and the pre-computed vectors may be used to generate processed data from target data derived from a target sample. The machine learning model may then be used on the processed data to generate the likelihood score related to the condition. As an example, subsets of the training dataset may be randomly selected, and the pre-computed vectors may be derived from the randomly-selected subsets of the training dataset. The pre-computed vectors may be applied to the target data to generate the processed data. In one use case, for example, the target data may be normalized using the pre-computed vectors.