Feature Processing Tradeoff Management in Machine Learning
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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, especially for infrequent but significant events like financial fraud detection, where traditional mechanisms for data transformations 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 automated data processing and model training, using a provider network with distributed computing and security features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning mechanisms are used for data transformations, then data processing can be performed, but the process becomes tedious and error-prone when applying similar transformations to many different variables
Solution Approach 1:
The patent segments the data transformation process into distinct, reusable transformation definitions that can be independently configured and applied to multiple variables. Each transformation is broken down into configurable parameters and execution steps, allowing systematic application across datasets without manual repetition.
Solution Approach 2:
The patent creates universal transformation mechanisms that can handle multiple types of data transformations through a single unified interface. The system supports various transformation types (normalization, binning, encoding, etc.) that can be applied to different variable types, eliminating the need for separate handling procedures for each transformation scenario.
2Measurement precision
If machine learning models are trained on large datasets to improve prediction accuracy for infrequent events, then prediction quality improves, but computational requirements and data handling complexity increase significantly
Solution Approach 1:
The patent performs preliminary data transformations and feature engineering before model training by defining and applying transformations to the entire dataset in advance. This preprocessing step prepares the data in an optimal format for training, reducing the computational burden during model execution and simplifying the handling of large datasets.
Solution Approach 2:
The system automatically applies defined transformations to variables based on configured parameters without requiring manual intervention for each variable. The transformation engine self-manages the application of transformations across the dataset, reducing human effort and minimizing errors in handling large volumes of data.
3Measurement precision
If more transformations are applied to input data variables to improve model training effectiveness, then model accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent implements dynamic transformation execution where the system can adaptively select and apply transformations based on data characteristics and model requirements. The transformation pipeline can be dynamically configured to apply only necessary transformations, optimizing the balance between model effectiveness and execution time.
Solution Approach 2:
The system allows dynamic adjustment of transformation parameters to optimize performance. By modifying transformation parameters such as binning intervals, normalization ranges, or encoding schemes, the system can achieve effective model training while controlling the computational overhead and execution time of transformations.
Data Source
AI summary
At a machine learning service, a set of candidate variables that can be used to train a model is identified, including at least one processed variable produced by a feature processing transformation. A cost estimate indicative of an effect of implementing the feature processing transformation on a performance metric associated with a prediction goal of the model is determined. Based at least in part on the cost estimate, a feature processing proposal that excludes the feature processing transformation is implemented.