View source for Google llc (20240104429). Model-Agnostic System for Automatic Investigation of the Impact of New Features on Performance of Machine Learning Models simplified abstract

=Model-Agnostic System for Automatic Investigation of the Impact of New Features on Performance of Machine Learning Models=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:Madhav Datt of Mountain View CA (US)|Madhav Datt of Mountain View CA (US)]][[Category:Madhav Datt of Mountain View CA (US)]]

[[:Category:Nikhil Shirish Ketkar of Bengaluru (IN)|Nikhil Shirish Ketkar of Bengaluru (IN)]][[Category:Nikhil Shirish Ketkar of Bengaluru (IN)]]

[[:Category:Arooshi Verma of Bengaluru (IN)|Arooshi Verma of Bengaluru (IN)]][[Category:Arooshi Verma of Bengaluru (IN)]]

==Model-Agnostic System for Automatic Investigation of the Impact of New Features on Performance of Machine Learning Models - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240104429 titled 'Model-Agnostic System for Automatic Investigation of the Impact of New Features on Performance of Machine Learning Models

==Simplified Explanation==

The patent application describes computing systems and methods that automatically investigate and analyze the impact of new features on the performance of a machine learning model by generating a ranked list of the most impactful features. 

* The computing system imports a training dataset and trains a machine learning model for the dataset.
* Baseline metrics for the model are generated, and correlations between features are identified.
* Features are grouped into clusters based on correlations, and the system determines the importance of each cluster and feature.
* A ranked list of signals and their importances is exported based on machine learning model performance lift.

== Potential Applications ==
This technology can be applied in various fields such as finance, healthcare, marketing, and e-commerce for optimizing machine learning models and improving predictive accuracy.

== Problems Solved ==
1. Identifying the most impactful features for machine learning models.
2. Streamlining the process of analyzing and investigating the impact of new features on model performance.

== Benefits ==
1. Improved model performance and predictive accuracy.
2. Automated feature analysis saves time and resources.
3. Enhanced understanding of feature importance in machine learning models.

== Potential Commercial Applications ==
Optimizing marketing campaigns, improving healthcare diagnostics, enhancing financial forecasting, and streamlining e-commerce recommendations.

== Possible Prior Art ==
One possible prior art could be traditional feature selection methods in machine learning, such as filter, wrapper, and embedded methods. These methods also aim to identify the most relevant features for model performance.

=== Unanswered Questions ===

=== How does this technology handle high-dimensional datasets? ===
The patent application does not specify how the system deals with high-dimensional datasets and whether it has any limitations in processing such data.

=== Can this technology adapt to different types of machine learning models? ===
It is unclear from the abstract whether the computing system is designed to work with specific types of machine learning models or if it can be applied across various model architectures.


==Original Abstract Submitted==

provided are computing systems, methods, and platforms that automatically investigate and analyze the impact of new features or signals on the performance of a machine learning model by producing a ranked list of the most impactful features from input of a set of candidate features. in particular, one example computing system can import a training dataset associated with a user. the computing system can train a machine learning model for the training dataset and generate baseline metrics for the machine learning model. correlations between features or signals in the training dataset can be identified and the features or signals can be grouped into clusters based on the correlations. the computing system can determine the importance of each cluster and each feature or signal. a ranked list of signals and their importances can be exported in decreasing order of machine learning model performance lift based on cluster importance and signal importance.



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